Crowd-sourced 3d points and point cloud alignment

ABSTRACT

Systems and methods are provided for vehicle navigation. In one implementation, a host vehicle-based sparse map feature harvester system may include at least one processor programmed to receive a plurality of images captured by a camera onboard the host vehicle as the host vehicle travels along a road segment in a first direction, wherein the plurality of images are representative of an environment of the host vehicle; detect one or more semantic features represented in one or more of the plurality of images, the one or more semantic features each being associated with a predetermined object type classification; identify at least one position descriptor associated with each of the detected one or more semantic features; identify three-dimensional feature points associated with one or more detected objects represented in at least one of the plurality of images; receive position information, for each of the plurality of images, wherein the position information is indicative of a position of the camera when each of the plurality of images was captured; and cause transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the identified at least one position descriptor associated with each of the detected one or more semantic features, the identified three-dimensional feature points, and the position information.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. Provisional Application No. 62/956,987, filed on Jan. 3, 2020; U.S. Provisional Application No. 62/956,993, filed on Jan. 3, 2020; U.S. Provisional Application No. 62/956,997, filed on Jan. 3, 2020; United States Provisional Application No. 62/957,017, filed on Jan. 3, 2020; U.S. Provisional Application No. 62/957,019, filed on Jan. 3, 2020; U.S. Provisional Application No. 62/957,028, filed on Jan. 3, 2020; U.S. Provisional Application No. 63/119,293, filed on Nov. 30, 2020; U.S. Provisional Application No. 63/120,533, filed on Dec. 2, 2020; and U.S. Provisional Application No. 63/120,536, filed on Dec. 2, 2020. The foregoing applications are incorporated herein by reference in their entirety.

BACKGROUND Technical Field

The present disclosure relates generally to autonomous vehicle navigation.

Background Information

As technology continues to advance, the goal of a fully autonomous vehicle that is capable of navigating on roadways is on the horizon. Autonomous vehicles may need to take into account a variety of factors and make appropriate decisions based on those factors to safely and accurately reach an intended destination. For example, an autonomous vehicle may need to process and interpret visual information (e.g., information captured from a camera) and may also use information obtained from other sources (e.g., from a GPS device, a speed sensor, an accelerometer, a suspension sensor, etc.). At the same time, in order to navigate to a destination, an autonomous vehicle may also need to identify its location within a particular roadway (e.g., a specific lane within a multi-lane road), navigate alongside other vehicles, avoid obstacles and pedestrians, observe traffic signals and signs, and travel from one road to another road at appropriate intersections or interchanges. Harnessing and interpreting vast volumes of information collected by an autonomous vehicle as the vehicle travels to its destination poses a multitude of design challenges. The sheer quantity of data (e.g., captured image data, map data, GPS data, sensor data, etc.) that an autonomous vehicle may need to analyze, access, and/or store poses challenges that can in fact limit or even adversely affect autonomous navigation. Furthermore, if an autonomous vehicle relies on traditional mapping technology to navigate, the sheer volume of data needed to store and update the map poses daunting challenges.

SUMMARY

Embodiments consistent with the present disclosure provide systems and methods for autonomous vehicle navigation. The disclosed embodiments may use cameras to provide autonomous vehicle navigation features. For example, consistent with the disclosed embodiments, the disclosed systems may include one, two, or more cameras that monitor the environment of a vehicle. The disclosed systems may provide a navigational response based on, for example, an analysis of images captured by one or more of the cameras.

In an embodiment, a host vehicle-based sparse map feature harvester system may include at least one processor. The processor may be programmed to receive a plurality of images captured by a camera onboard the host vehicle as the host vehicle travels along a road segment in a first direction, wherein the plurality of images are representative of an environment of the host vehicle; detect one or more semantic features represented in one or more of the plurality of images, the one or more semantic features each being associated with a predetermined object type classification; identify at least one position descriptor associated with each of the detected one or more semantic features; and identify three-dimensional feature points associated with one or more detected objects represented in at least one of the plurality of images. The processor may further be programmed to receive position information, for each of the plurality of images, wherein the position information is indicative of a position of the camera when each of the plurality of images was captured; and cause transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the identified at least one position descriptor associated with each of the detected one or more semantic features, the identified three-dimensional feature points, and the position information.

In an embodiment, a method for harvesting sparse map features by a host vehicle may include receiving a plurality of images captured by a camera onboard the host vehicle as the host vehicle travels along a road segment in a first direction, wherein the plurality of images are representative of an environment of the host vehicle; detecting one or more semantic features represented in one or more of the plurality of images, the one or more semantic features each being associated with a predetermined object type classification; identifying at least one position descriptor associated with each of the detected one or more semantic features; and identifying three-dimensional feature points associated with a one or more detected objects represented in at least one of the plurality of images. The method may further include receiving position information, for each of the plurality of images, wherein the position information is indicative of a position of the camera when each of the plurality of images was captured; and causing transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the identified at least one position descriptor associated with each of the detected one or more semantic features, the identified three-dimensional feature points, and the position information.

In an embodiment, a sparse map generator system for creating maps used in navigating autonomous or partially autonomous vehicles may include at least one processor. The processor may be programmed to receive first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction, wherein the first drive information includes a first plurality of three-dimensional feature points associated with objects detected by navigation systems of the first plurality of vehicles; and receive second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction, wherein the second drive information includes a second plurality of three dimensional feature points associated with objects detected by navigation systems of the second plurality of vehicles. The processor may further be programmed to correlate one or more of the first plurality of three-dimensional feature points with one or more of the second plurality of three-dimensional feature points; and generate a sparse map based on the correlation of the first plurality of three dimensional feature points and the second plurality of three-dimensional feature points, the sparse map including at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction.

In an embodiment, a method for creating maps used in navigating autonomous or partially autonomous vehicles may include receiving first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction, wherein the first drive information includes a first plurality of three-dimensional feature points associated with objects detected by navigation systems of the first plurality of vehicles; receiving second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction, wherein the second drive information includes a second plurality of three dimensional feature points associated with objects detected by navigation systems of the second plurality of vehicles; correlating one or more of the first plurality of three-dimensional feature points with one or more of the second plurality of three-dimensional feature points; and generating a sparse map based on the correlation of the first plurality of three dimensional feature points and the second plurality of three-dimensional feature points, the sparse map including at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction.

In an embodiment, a navigation system for an autonomous or partially autonomous host vehicle may include at least one processor. The processor may be programmed to receive, from an entity remotely located relative to the host vehicle, a sparse map associated with at least one road segment. The sparse map may include a first plurality of mapped navigational features generated based on drive information previously collected from a first plurality of vehicles that traveled in a first direction along the at least one road segment, and a second plurality of mapped navigational features generated based on drive information previously collected from a second plurality of vehicles that traveled in a second direction along the at least one road segment, wherein the second direction is opposite to the first direction, and wherein the first plurality of mapped navigational features and the second plurality of mapped navigational features are correlated within a common coordinate system. The processor may further be programmed to receive, from a camera associated with the host vehicle, a first plurality of images and a second plurality of images representative of an environment of the host vehicle as the host vehicle travels along the at least one road segment in the first direction; determine a first navigational action for the host vehicle based on analysis of at least one of the first plurality of images and based on the first plurality of mapped navigational features; and cause one or more actuators associated with the host vehicle to implement the first navigational action. The processor may further be configured to determine a second navigational action for the host vehicle based on analysis of the second plurality of images and based on the second plurality of mapped navigational features; and cause the one or more actuators associated with the host vehicle to implement the second navigational action.

In an embodiment, a method for navigating an autonomous or partially autonomous host vehicle may include receiving, from an entity remotely located relative to the host vehicle, a sparse map associated with at least one road segment. The sparse map may include a first plurality of mapped navigational features generated based on drive information previously collected from a first plurality of vehicles that traveled in a first direction along the at least one road segment, and a second plurality of mapped navigational features generated based on drive information previously collected from a second plurality of vehicles that traveled in a second direction along the at least one road segment, wherein the second direction is opposite to the first direction, and wherein the first plurality of mapped navigational features and the second plurality of mapped navigational features are correlated within a common coordinate system. The method may further include receiving, from a camera associated with the host vehicle, a first plurality of images and a second plurality of images representative of an environment of the host vehicle as the host vehicle travels along the at least one road segment in the first direction; determining a first navigational action for the host vehicle based on analysis of at least one of the first plurality of images and based on the first plurality of mapped navigational features; and causing one or more actuators associated with the host vehicle to implement the first navigational action. The method may further include determining a second navigational action for the host vehicle based on analysis of the second plurality of images and based on the second plurality of mapped navigational features; and causing the one or more actuators associated with the host vehicle to implement the second navigational action.

In an embodiment, a navigation system for creating maps used in navigating autonomous or partially autonomous vehicles may include at least one processor. The processor may be programmed to receive drive information from each of a plurality of vehicles distributed across a plurality of vehicle groups that traverse a road junction, the road junction including a plurality of entrances and a plurality of exits associated with each of the plurality of entrances. Each vehicle group may include one or more vehicles that traverse a different entrance-exit combination associated with the road junction. The drive information from each of the plurality of vehicles may include three-dimensional feature points associated with objects detected by analyzing images captured as a particular vehicle traversed a particular entrance-exit combination of the road junction. The processor may further be programmed to, for each of the entrance-exit combinations, align the three-dimensional feature points received in the drive information collected from the one or more vehicles that traversed that entrance-exit combination to generate a plurality of aligned three-dimensional feature point groups, one for each entrance-exit combination of the road junction; correlate one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included in every other aligned three-dimensional feature point group from among the plurality of aligned three-dimensional feature point groups; and generate a sparse map based on the correlation of the one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included every other aligned three-dimensional feature point group, the sparse map including at least one target trajectory associated with each of the entrance-exit combinations of the road junction.

In an embodiment, a method for creating maps used in navigating autonomous or partially autonomous vehicles may include receiving drive information from each of a plurality of vehicles distributed across a plurality of vehicle groups that traverse a road junction, the road junction including a plurality of entrances and a plurality of exits associated with each of the plurality of entrances. Each vehicle group includes one or more vehicles that traverse a different entrance-exit combination associated with the road junction. The drive information from each of the plurality of vehicles includes three-dimensional feature points associated with objects detected by analyzing images captured as a particular vehicle traversed a particular entrance-exit combination of the road junction. The method may further include, for each of the entrance-exit combinations, aligning the three-dimensional feature points received in the drive information collected from the one or more vehicles that traversed that entrance-exit combination to generate a plurality of aligned three-dimensional feature point groups, one for each entrance-exit combination of the road junction; correlating one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included in every other aligned three-dimensional feature point group from among the plurality of aligned three-dimensional feature point groups; and generating a sparse map based on the correlation of the one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included every other aligned three-dimensional feature point group, the sparse map including at least one target trajectory associated with each of the entrance-exit combinations of the road junction.

In an embodiment, a host vehicle-based sparse map feature harvester system may include at least one processor. The processor may be programmed to receive a first image captured by a forward-facing camera onboard the host vehicle, as the host vehicle travels along a road segment in a first direction, wherein the first image is representative of an environment forward of the host vehicle; and receive a second image captured by a rearward-facing camera onboard the host vehicle, as the host vehicle travels along the road segment in the first direction, wherein the second image is representative of an environment behind the host vehicle. The processor may detect a first semantic feature represented in the first image, wherein the first semantic feature is associated with a predetermined object type classification; identify at least one position descriptor associated with the first semantic feature represented in the first image captured by the forward-facing camera; detect a second semantic feature represented in the second image, wherein the second semantic feature is associated with a predetermined object type classification; and identify at least one position descriptor associated with the second semantic feature represented in the second image captured by the rearward-facing camera. The processor may then receive position information indicative of a position of the forward-facing camera when the first image was captured and indicative of a position of the rearward-facing camera when the second image was captured; and cause transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the at least one position descriptor associated with the first semantic feature, the at least one position descriptor associated with the second semantic feature, and the position information.

In an embodiment, a method for harvesting sparse map features by a host vehicle may include receiving a first image captured by a forward-facing camera onboard the host vehicle, as the host vehicle travels along a road segment in a first direction, wherein the first image is representative of an environment forward of the host vehicle; and receiving a second image captured by a rearward-facing camera onboard the host vehicle, as the host vehicle travels along the road segment in the first direction, wherein the second image is representative of an environment behind the host vehicle. The method may further include detecting a first semantic feature represented in the first image, wherein the first semantic feature is associated with a predetermined object type classification; identifying at least one position descriptor associated with the first semantic feature represented in the first image captured by the forward-facing camera; detecting a second semantic feature represented in the second image, wherein the second semantic feature is associated with a predetermined object type classification; and identifying at least one position descriptor associated with the second semantic feature represented in the second image captured by the rearward-facing camera. The method may further include receiving position information indicative of a position of the forward-facing camera when the first image was captured and indicative of a position of the rearward-facing camera when the second image was captured; and causing transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the at least one position descriptor associated with the first semantic feature, the at least one position descriptor associated with the second semantic feature, and the position information.

In an embodiment, a sparse map generator system for creating maps used in navigating autonomous or partially autonomous vehicles may include at least one processor. The at least one processor may be programmed to receive first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction; receive second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction; and receive, from at least one vehicle equipped with a forward-facing camera and a rearward-facing camera, third drive information for the road segment, wherein the third drive information includes: a position descriptor associated with a first semantic feature detected based on analysis of a forward-facing image captured by the forward-facing camera; and a position descriptor associated with a second semantic feature detected based on analysis of a rearward-facing image captured by the rearward-facing camera. The processor may be programmed to correlate one or more aspects of the first drive information and the second drive information based, at least in part, upon the position descriptor associated with the first semantic feature detected based on analysis of the forward-facing image and upon the position descriptor associated with the second semantic feature detected based on analysis of the rearward-facing image captured by the rearward-facing camera; and generate the sparse map based, at least in part, on the correlation of the first drive information and the second drive information, the sparse map including at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction.

In an embodiment, a method for creating maps used in navigating autonomous or partially autonomous vehicles may include receiving first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction; receiving second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction; and receiving, from at least one vehicle equipped with a forward-facing camera and a rearward-facing camera, third drive information for the road segment, wherein the third drive information includes: a position descriptor associated with a first semantic feature detected based on analysis of a forward-facing image captured by the forward-facing camera; and a position descriptor associated with a second semantic feature detected based on analysis of a rearward-facing image captured by the rearward-facing camera. The method may further include correlating one or more aspects of the first drive information and the second drive information based, at least in part, upon the position descriptor associated with the first semantic feature detected based on analysis of the forward-facing image and upon the position descriptor associated with the second semantic feature detected based on analysis of the rearward-facing image captured by the rearward-facing camera; and generating the sparse map based, at least in part, on the correlation of the first drive information and the second drive information, the sparse map including at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction.

In an embodiment, a host vehicle-based sparse map feature harvester system may include at least one processor. The processor may be programmed to receive a first image captured by a forward-facing camera onboard the host vehicle, as the host vehicle travels along a road segment in a first direction, wherein the first image is representative of an environment forward of the host vehicle; and receive a second image captured by a rearward-facing camera onboard the host vehicle, as the host vehicle travels along the road segment in the first direction, wherein the second image is representative of an environment behind the host vehicle. The processor may further be programmed to detect at least one object represented in the first image; identify at least one front side two-dimensional feature point, the at least one front side two-dimensional feature point being associated with the at least one object represented in the first image; detect a representation of the at least one object in the second image; and identify at least one rear side two-dimensional feature point, the at least one rear side two-dimensional feature point being associated with the at least one object represented in the second image. The processor may be programmed to receive position information indicative of a position of the forward-facing camera when the first image was captured and indicative of a position of the rearward-facing camera when the second image was captured; and cause transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the at least one front side two-dimensional feature point, the at least one rear side two-dimensional feature point, and the position information.

In an embodiment, a method for harvesting sparse map features by a host vehicle may include receiving a first image captured by a forward-facing camera onboard the host vehicle, as the host vehicle travels along a road segment in a first direction, wherein the first image is representative of an environment forward of the host vehicle; and receiving a second image captured by a rearward-facing camera onboard the host vehicle, as the host vehicle travels along the road segment in the first direction, wherein the second image is representative of an environment behind the host vehicle. The method may further include detecting at least one object represented in the first image; identifying at least one front side two-dimensional feature point, the at least one front side two-dimensional feature point being associated with the at least one object represented in the first image; detecting a representation of the at least one object in the second image; and identifying at least one rear side two-dimensional feature point, the at least one rear side two-dimensional feature point being associated with the at least one object represented in the second image. The method may then include receiving position information indicative of a position of the forward-facing camera when the first image was captured and indicative of a position of the rearward-facing camera when the second image was captured; and causing transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the at least one front side two-dimensional feature point, the at least one rear side two-dimensional feature point, and the position information.

In an embodiment, a sparse map generator system for creating maps used in navigating autonomous or partially autonomous vehicles may include at least one processor. The processor may be programmed to receive first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction; receive second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction; and receive, from at least one vehicle equipped with a forward-facing camera and a rearward-facing camera, third drive information for the road segment, wherein the third drive information includes: at least one front side two-dimensional feature point generated based on analysis of a representation of an object in a forward-facing image captured by the forward-facing camera; at least one rear side two-dimensional feature point generated based on analysis of a representation of the object in a rearward-facing image captured by the rearward-facing camera. The processor may further be programmed to correlate one or more aspects of the first drive information and the second drive information based, at least in part, upon the at least one front side two-dimensional feature point and upon the at least one rear side two-dimensional feature point included in the third drive information; and generate the sparse map based, at least in part, on the correlation of the first drive information and the second drive information, the sparse map including at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction.

In an embodiment, a method for creating maps used in navigating autonomous or partially autonomous vehicles may include receiving first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction; receiving second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction; and receiving, from at least one vehicle equipped with a forward-facing camera and a rearward-facing camera, third drive information for the road segment, wherein the third drive information includes: at least one front side two-dimensional feature point generated based on analysis of a representation of an object in a forward-facing image captured by the forward-facing camera; at least one rear side two-dimensional feature point generated based on analysis of a representation of the object in a rearward-facing image captured by the rearward-facing camera. The method may further include correlating one or more aspects of the first drive information and the second drive information based, at least in part, upon the at least one front side two-dimensional feature point and upon the at least one rear side two-dimensional feature point included in the third drive information; and generating the sparse map based, at least in part, on the correlation of the first drive information and the second drive information, the sparse map including at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction.

Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processing device and perform any of the methods described herein.

The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments. In the drawings:

FIG. 1 is a diagrammatic representation of an exemplary system consistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplary vehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle and system shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodiment of a vehicle including a system consistent with the disclosed embodiments.

FIG. 2D is a diagrammatic top view representation of yet another embodiment of a vehicle including a system consistent with the disclosed embodiments.

FIG. 2E is a diagrammatic top view representation of yet another embodiment of a vehicle including a system consistent with the disclosed embodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle control systems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicle including a rearview mirror and a user interface for a vehicle imaging system consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that is configured to be positioned behind a rearview mirror and against a vehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from a different perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that is configured to be positioned behind a rearview mirror and against a vehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to store instructions for performing one or more operations consistent with the disclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one or more navigational responses based on monocular image analysis consistent with disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one or more vehicles and/or pedestrians in a set of images consistent with the disclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting road marks and/or lane geometry information in a set of images consistent with the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detecting traffic lights in a set of images consistent with the disclosed embodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one or more navigational responses based on a vehicle path consistent with the disclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determining whether a leading vehicle is changing lanes consistent with the disclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one or more navigational responses based on stereo image analysis consistent with the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one or more navigational responses based on an analysis of three sets of images consistent with the disclosed embodiments.

FIG. 8 shows a sparse map for providing autonomous vehicle navigation, consistent with the disclosed embodiments.

FIG. 9A illustrates a polynomial representation of a portions of a road segment consistent with the disclosed embodiments.

FIG. 9B illustrates a curve in three-dimensional space representing a target trajectory of a vehicle, for a particular road segment, included in a sparse map consistent with the disclosed embodiments.

FIG. 10 illustrates example landmarks that may be included in sparse map consistent with the disclosed embodiments.

FIG. 11A shows polynomial representations of trajectories consistent with the disclosed embodiments.

FIGS. 11B and 11C show target trajectories along a multi-lane road consistent with disclosed embodiments.

FIG. 11D shows an example road signature profile consistent with disclosed embodiments.

FIG. 12 is a schematic illustration of a system that uses crowd sourcing data received from a plurality of vehicles for autonomous vehicle navigation, consistent with the disclosed embodiments.

FIG. 13 illustrates an example autonomous vehicle road navigation model represented by a plurality of three dimensional splines, consistent with the disclosed embodiments.

FIG. 14 shows a map skeleton generated from combining location information from many drives, consistent with the disclosed embodiments.

FIG. 15 shows an example of a longitudinal alignment of two drives with example signs as landmarks, consistent with the disclosed embodiments.

FIG. 16 shows an example of a longitudinal alignment of many drives with an example sign as a landmark, consistent with the disclosed embodiments.

FIG. 17 is a schematic illustration of a system for generating drive data using a camera, a vehicle, and a server, consistent with the disclosed embodiments.

FIG. 18 is a schematic illustration of a system for crowdsourcing a sparse map, consistent with the disclosed embodiments.

FIG. 19 is a flowchart showing an exemplary process for generating a sparse map for autonomous vehicle navigation along a road segment, consistent with the disclosed embodiments.

FIG. 20 illustrates a block diagram of a server consistent with the disclosed embodiments.

FIG. 21 illustrates a block diagram of a memory consistent with the disclosed embodiments.

FIG. 22 illustrates a process of clustering vehicle trajectories associated with vehicles, consistent with the disclosed embodiments.

FIG. 23 illustrates a navigation system for a vehicle, which may be used for autonomous navigation, consistent with the disclosed embodiments.

FIGS. 24A, 24B, 24C, and 24D illustrate exemplary lane marks that may be detected consistent with the disclosed embodiments.

FIG. 24E shows exemplary mapped lane marks consistent with the disclosed embodiments.

FIG. 24F shows an exemplary anomaly associated with detecting a lane mark consistent with the disclosed embodiments.

FIG. 25A shows an exemplary image of a vehicle's surrounding environment for navigation based on the mapped lane marks consistent with the disclosed embodiments.

FIG. 25B illustrates a lateral localization correction of a vehicle based on mapped lane marks in a road navigation model consistent with the disclosed embodiments.

FIGS. 25C and 25D provide conceptual representations of a localization technique for locating a host vehicle along a target trajectory using mapped features included in a sparse map.

FIG. 26A is a flowchart showing an exemplary process for mapping a lane mark for use in autonomous vehicle navigation consistent with disclosed embodiments.

FIG. 26B is a flowchart showing an exemplary process for autonomously navigating a host vehicle along a road segment using mapped lane marks consistent with disclosed embodiments.

FIG. 27 illustrates an example image that may be captured by a host vehicle for aligning drive information, consistent with the disclosed embodiments.

FIG. 28 illustrates an example 3D point that may be obtained by a host vehicle, consistent with the disclosed embodiments.

FIG. 29 illustrates an example system for generating a sparse map based on 3D point clouds, consistent with the disclosed embodiments.

FIG. 30 illustrates an example alignment process that may be performed for points captured from different drive directions, consistent with the disclosed embodiments.

FIG. 31 is a flowchart showing an example process for harvesting data for a sparse map, consistent with the disclosed embodiments.

FIG. 32 is a flowchart showing an example process for creating maps used in navigating autonomous or partially autonomous vehicles, consistent with the disclosed embodiments.

FIG. 33 is an illustration of an example road segment along which a host vehicle may navigate, consistent with the disclosed embodiments.

FIG. 34 illustrates an example sparse map having two-way aligned trajectories, consistent with the disclosed embodiments.

FIG. 35 is a flowchart showing an example process for navigating an autonomous or partially autonomous host vehicle, consistent with the disclosed embodiments.

FIG. 36A illustrates an example junction that may be traversed by one or more vehicles, consistent with the disclosed embodiments.

FIG. 36B illustrates various entrance and exit combinations for a junction that may be traveled by a host vehicle, consistent with the disclosed embodiments.

FIG. 37 illustrates example three-dimensional points that may be collected by a host vehicle while traversing a junction, consistent with the disclosed embodiments.

FIG. 38 illustrates an example alignment of three-dimensional points collected along a common target trajectory, consistent with the disclosed embodiments.

FIG. 39 illustrates an example process for generating a sparse map based on correlated 3D points from multiple entrance-exit combinations, consistent with the disclosed embodiments.

FIG. 40 illustrates an example alignment of two sets of aligned 3D points, consistent with the disclosed embodiments.

FIG. 41 is a flowchart showing an example process for creating maps used in navigating autonomous or partially autonomous vehicles, consistent with the disclosed embodiments.

FIG. 42A illustrates an example road segment along which drive information from multiple directions may be aligned using rear-facing cameras, consistent with the disclosed embodiments.

FIG. 42B illustrates an example image that may be captured by a front-facing camera of a host vehicle, consistent with the disclosed embodiments.

FIG. 43A illustrates an example scenario for a host vehicle to capture a rear-facing image along a road segment, consistent with the disclosed embodiments.

FIG. 43B illustrates an example rear-facing image that may be taken by a host vehicle, consistent with the disclosed embodiments.

FIG. 44 illustrates example sets of drive information captured by vehicles traveling in opposite directions along a road segment, consistent with the disclosed embodiments.

FIG. 45 is a flowchart showing an example process for harvesting data for a sparse map, consistent with the disclosed embodiments.

FIG. 46 is a flowchart showing an example process for creating maps used in navigating autonomous or partially autonomous vehicles, consistent with the disclosed embodiments.

FIG. 47 is a flowchart showing an example process for harvesting data for a sparse map, consistent with the disclosed embodiments.

FIG. 48 is a flowchart showing an example process for creating maps used in navigating autonomous or partially autonomous vehicles, consistent with the disclosed embodiments.

FIG. 49 illustrates an exemplary system for automatically generating a navigational map relative to one or more road segments, consistent with the disclosed embodiments.

FIGS. 50A, 50B, and 50C illustrate an exemplary process for collecting navigational information, consistent with disclosed embodiments.

FIG. 51 is a flowchart showing an exemplary process for automatically generating a navigational map relative to one or more road segments, consistent with the disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations and other implementations are possible. For example, substitutions, additions or modifications may be made to the components illustrated in the drawings, and the illustrative methods described herein may be modified by substituting, reordering, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope is defined by the appended claims.

Autonomous Vehicle Overview

As used throughout this disclosure, the term “autonomous vehicle” refers to a vehicle capable of implementing at least one navigational change without driver input. A “navigational change” refers to a change in one or more of steering, braking, or acceleration of the vehicle. To be autonomous, a vehicle need not be fully automatic (e.g., fully operation without a driver or without driver input). Rather, an autonomous vehicle includes those that can operate under driver control during certain time periods and without driver control during other time periods. Autonomous vehicles may also include vehicles that control only some aspects of vehicle navigation, such as steering (e.g., to maintain a vehicle course between vehicle lane constraints), but may leave other aspects to the driver (e.g., braking). In some cases, autonomous vehicles may handle some or all aspects of braking, speed control, and/or steering of the vehicle.

As human drivers typically rely on visual cues and observations to control a vehicle, transportation infrastructures are built accordingly, with lane markings, traffic signs, and traffic lights are all designed to provide visual information to drivers. In view of these design characteristics of transportation infrastructures, an autonomous vehicle may include a camera and a processing unit that analyzes visual information captured from the environment of the vehicle. The visual information may include, for example, components of the transportation infrastructure (e.g., lane markings, traffic signs, traffic lights, etc.) that are observable by drivers and other obstacles (e.g., other vehicles, pedestrians, debris, etc.). Additionally, an autonomous vehicle may also use stored information, such as information that provides a model of the vehicle's environment when navigating. For example, the vehicle may use GPS data, sensor data (e.g., from an accelerometer, a speed sensor, a suspension sensor, etc.), and/or other map data to provide information related to its environment while the vehicle is traveling, and the vehicle (as well as other vehicles) may use the information to localize itself on the model.

In some embodiments in this disclosure, an autonomous vehicle may use information obtained while navigating (e.g., from a camera, GPS device, an accelerometer, a speed sensor, a suspension sensor, etc.). In other embodiments, an autonomous vehicle may use information obtained from past navigations by the vehicle (or by other vehicles) while navigating. In yet other embodiments, an autonomous vehicle may use a combination of information obtained while navigating and information obtained from past navigations. The following sections provide an overview of a system consistent with the disclosed embodiments, followed by an overview of a forward-facing imaging system and methods consistent with the system. The sections that follow disclose systems and methods for constructing, using, and updating a sparse map for autonomous vehicle navigation.

System Overview

FIG. 1 is a block diagram representation of a system 100 consistent with the exemplary disclosed embodiments. System 100 may include various components depending on the requirements of a particular implementation. In some embodiments, system 100 may include a processing unit 110, an image acquisition unit 120, a position sensor 130, one or more memory units 140, 150, a map database 160, a user interface 170, and a wireless transceiver 172. Processing unit 110 may include one or more processing devices. In some embodiments, processing unit 110 may include an applications processor 180, an image processor 190, or any other suitable processing device. Similarly, image acquisition unit 120 may include any number of image acquisition devices and components depending on the requirements of a particular application. In some embodiments, image acquisition unit 120 may include one or more image capture devices (e.g., cameras), such as image capture device 122, image capture device 124, and image capture device 126. System 100 may also include a data interface 128 communicatively connecting processing device 110 to image acquisition device 120. For example, data interface 128 may include any wired and/or wireless link or links for transmitting image data acquired by image accusation device 120 to processing unit 110.

Wireless transceiver 172 may include one or more devices configured to exchange transmissions over an air interface to one or more networks (e.g., cellular, the Internet, etc.) by use of a radio frequency, infrared frequency, magnetic field, or an electric field. Wireless transceiver 172 may use any known standard to transmit and/or receive data (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.). Such transmissions can include communications from the host vehicle to one or more remotely located servers. Such transmissions may also include communications (one-way or two-way) between the host vehicle and one or more target vehicles in an environment of the host vehicle (e.g., to facilitate coordination of navigation of the host vehicle in view of or together with target vehicles in the environment of the host vehicle), or even a broadcast transmission to unspecified recipients in a vicinity of the transmitting vehicle.

Both applications processor 180 and image processor 190 may include various types of processing devices. For example, either or both of applications processor 180 and image processor 190 may include a microprocessor, preprocessors (such as an image preprocessor), a graphics processing unit (GPU), a central processing unit (CPU), support circuits, digital signal processors, integrated circuits, memory, or any other types of devices suitable for running applications and for image processing and analysis. In some embodiments, applications processor 180 and/or image processor 190 may include any type of single or multi-core processor, mobile device microcontroller, central processing unit, etc. Various processing devices may be used, including, for example, processors available from manufacturers such as Intel®, AMD®, etc., or GPUs available from manufacturers such as NVIDIA®, ATI®, etc. and may include various architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor 190 may include any of the EyeQ series of processor chips available from Mobileye®. These processor designs each include multiple processing units with local memory and instruction sets. Such processors may include video inputs for receiving image data from multiple image sensors and may also include video out capabilities. In one example, the EyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2® architecture consists of two floating point, hyper-thread 32-bit RISC CPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), three Vector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller, 128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bit Video output controllers, 16 channels DMA and several peripherals. The MIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the second MIPS34K CPU and the multi-channel DMA as well as the other peripherals. The five VCEs, three VMP® and the MIPS34K CPU can perform intensive vision computations required by multi-function bundle applications. In another example, the EyeQ3®, which is a third generation processor and is six times more powerful that the EyeQ2®, may be used in the disclosed embodiments. In other examples, the EyeQ4® and/or the EyeQ5® may be used in the disclosed embodiments. Of course, any newer or future EyeQ processing devices may also be used together with the disclosed embodiments.

Any of the processing devices disclosed herein may be configured to perform certain functions. Configuring a processing device, such as any of the described EyeQ processors or other controller or microprocessor, to perform certain functions may include programming of computer executable instructions and making those instructions available to the processing device for execution during operation of the processing device. In some embodiments, configuring a processing device may include programming the processing device directly with architectural instructions. For example, processing devices such as field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and the like may be configured using, for example, one or more hardware description languages (HDLs).

In other embodiments, configuring a processing device may include storing executable instructions on a memory that is accessible to the processing device during operation. For example, the processing device may access the memory to obtain and execute the stored instructions during operation. In either case, the processing device configured to perform the sensing, image analysis, and/or navigational functions disclosed herein represents a specialized hardware-based system in control of multiple hardware based components of a host vehicle.

While FIG. 1 depicts two separate processing devices included in processing unit 110, more or fewer processing devices may be used. For example, in some embodiments, a single processing device may be used to accomplish the tasks of applications processor 180 and image processor 190. In other embodiments, these tasks may be performed by more than two processing devices. Further, in some embodiments, system 100 may include one or more of processing unit 110 without including other components, such as image acquisition unit 120.

Processing unit 110 may comprise various types of devices. For example, processing unit 110 may include various devices, such as a controller, an image preprocessor, a central processing unit (CPU), a graphics processing unit (GPU), support circuits, digital signal processors, integrated circuits, memory, or any other types of devices for image processing and analysis. The image preprocessor may include a video processor for capturing, digitizing and processing the imagery from the image sensors. The CPU may comprise any number of microcontrollers or microprocessors. The GPU may also comprise any number of microcontrollers or microprocessors. The support circuits may be any number of circuits generally well known in the art, including cache, power supply, clock and input-output circuits. The memory may store software that, when executed by the processor, controls the operation of the system. The memory may include databases and image processing software. The memory may comprise any number of random access memories, read only memories, flash memories, disk drives, optical storage, tape storage, removable storage and other types of storage. In one instance, the memory may be separate from the processing unit 110. In another instance, the memory may be integrated into the processing unit 110.

Each memory 140, 150 may include software instructions that when executed by a processor (e.g., applications processor 180 and/or image processor 190), may control operation of various aspects of system 100. These memory units may include various databases and image processing software, as well as a trained system, such as a neural network, or a deep neural network, for example. The memory units may include random access memory (RAM), read only memory (ROM), flash memory, disk drives, optical storage, tape storage, removable storage and/or any other types of storage. In some embodiments, memory units 140, 150 may be separate from the applications processor 180 and/or image processor 190. In other embodiments, these memory units may be integrated into applications processor 180 and/or image processor 190.

Position sensor 130 may include any type of device suitable for determining a location associated with at least one component of system 100. In some embodiments, position sensor 130 may include a GPS receiver. Such receivers can determine a user position and velocity by processing signals broadcasted by global positioning system satellites. Position information from position sensor 130 may be made available to applications processor 180 and/or image processor 190.

In some embodiments, system 100 may include components such as a speed sensor (e.g., a tachometer, a speedometer) for measuring a speed of vehicle 200 and/or an accelerometer (either single axis or multiaxis) for measuring acceleration of vehicle 200.

User interface 170 may include any device suitable for providing information to or for receiving inputs from one or more users of system 100. In some embodiments, user interface 170 may include user input devices, including, for example, a touchscreen, microphone, keyboard, pointer devices, track wheels, cameras, knobs, buttons, etc. With such input devices, a user may be able to provide information inputs or commands to system 100 by typing instructions or information, providing voice commands, selecting menu options on a screen using buttons, pointers, or eye-tracking capabilities, or through any other suitable techniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devices configured to provide and receive information to or from a user and process that information for use by, for example, applications processor 180. In some embodiments, such processing devices may execute instructions for recognizing and tracking eye movements, receiving and interpreting voice commands, recognizing and interpreting touches and/or gestures made on a touchscreen, responding to keyboard entries or menu selections, etc. In some embodiments, user interface 170 may include a display, speaker, tactile device, and/or any other devices for providing output information to a user.

Map database 160 may include any type of database for storing map data useful to system 100. In some embodiments, map database 160 may include data relating to the position, in a reference coordinate system, of various items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc. Map database 160 may store not only the locations of such items, but also descriptors relating to those items, including, for example, names associated with any of the stored features. In some embodiments, map database 160 may be physically located with other components of system 100. Alternatively or additionally, map database 160 or a portion thereof may be located remotely with respect to other components of system 100 (e.g., processing unit 110). In such embodiments, information from map database 160 may be downloaded over a wired or wireless data connection to a network (e.g., over a cellular network and/or the Internet, etc.). In some cases, map database 160 may store a sparse data model including polynomial representations of certain road features (e.g., lane markings) or target trajectories for the host vehicle. Systems and methods of generating such a map are discussed below with references to FIGS. 8-19 .

Image capture devices 122, 124, and 126 may each include any type of device suitable for capturing at least one image from an environment. Moreover, any number of image capture devices may be used to acquire images for input to the image processor. Some embodiments may include only a single image capture device, while other embodiments may include two, three, or even four or more image capture devices. Image capture devices 122, 124, and 126 will be further described with reference to FIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated into various different platforms. In some embodiments, system 100 may be included on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200 may be equipped with a processing unit 110 and any of the other components of system 100, as described above relative to FIG. 1 . While in some embodiments vehicle 200 may be equipped with only a single image capture device (e.g., camera), in other embodiments, such as those discussed in connection with FIGS. 2B-2E, multiple image capture devices may be used. For example, either of image capture devices 122 and 124 of vehicle 200, as shown in FIG. 2A, may be part of an ADAS (Advanced Driver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the image acquisition unit 120 may be positioned at any suitable location. In some embodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122 may be located in the vicinity of the rearview mirror. This position may provide a line of sight similar to that of the driver of vehicle 200, which may aid in determining what is and is not visible to the driver. Image capture device 122 may be positioned at any location near the rearview mirror, but placing image capture device 122 on the driver side of the mirror may further aid in obtaining images representative of the driver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit 120 may also be used. For example, image capture device 124 may be located on or in a bumper of vehicle 200. Such a location may be especially suitable for image capture devices having a wide field of view. The line of sight of bumper-located image capture devices can be different from that of the driver and, therefore, the bumper image capture device and driver may not always see the same objects. The image capture devices (e.g., image capture devices 122, 124, and 126) may also be located in other locations. For example, the image capture devices may be located on or in one or both of the side mirrors of vehicle 200, on the roof of vehicle 200, on the hood of vehicle 200, on the trunk of vehicle 200, on the sides of vehicle 200, mounted on, positioned behind, or positioned in front of any of the windows of vehicle 200, and mounted in or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include various other components of system 100. For example, processing unit 110 may be included on vehicle 200 either integrated with or separate from an engine control unit (ECU) of the vehicle. Vehicle 200 may also be equipped with a position sensor 130, such as a GPS receiver and may also include a map database 160 and memory units 140 and 150.

As discussed earlier, wireless transceiver 172 may and/or receive data over one or more networks (e.g., cellular networks, the Internet, etc.). For example, wireless transceiver 172 may upload data collected by system 100 to one or more servers, and download data from the one or more servers. Via wireless transceiver 172, system 100 may receive, for example, periodic or on demand updates to data stored in map database 160, memory 140, and/or memory 150. Similarly, wireless transceiver 172 may upload any data (e.g., images captured by image acquisition unit 120, data received by position sensor 130 or other sensors, vehicle control systems, etc.) from by system 100 and/or any data processed by processing unit 110 to the one or more servers.

System 100 may upload data to a server (e.g., to the cloud) based on a privacy level setting. For example, system 100 may implement privacy level settings to regulate or limit the types of data (including metadata) sent to the server that may uniquely identify a vehicle and or driver/owner of a vehicle. Such settings may be set by user via, for example, wireless transceiver 172, be initialized by factory default settings, or by data received by wireless transceiver 172.

In some embodiments, system 100 may upload data according to a “high” privacy level, and under setting a setting, system 100 may transmit data (e.g., location information related to a route, captured images, etc.) without any details about the specific vehicle and/or driver/owner. For example, when uploading data according to a “high” privacy setting, system 100 may not include a vehicle identification number (VIN) or a name of a driver or owner of the vehicle, and may instead of transmit data, such as captured images and/or limited location information related to a route.

Other privacy levels are contemplated. For example, system 100 may transmit data to a server according to an “intermediate” privacy level and include additional information not included under a “high” privacy level, such as a make and/or model of a vehicle and/or a vehicle type (e.g., a passenger vehicle, sport utility vehicle, truck, etc.). In some embodiments, system 100 may upload data according to a “low” privacy level. Under a “low” privacy level setting, system 100 may upload data and include information sufficient to uniquely identify a specific vehicle, owner/driver, and/or a portion or entirely of a route traveled by the vehicle. Such “low” privacy level data may include one or more of, for example, a VIN, a driver/owner name, an origination point of a vehicle prior to departure, an intended destination of the vehicle, a make and/or model of the vehicle, a type of the vehicle, etc.

FIG. 2A is a diagrammatic side view representation of an exemplary vehicle imaging system consistent with the disclosed embodiments. FIG. 2B is a diagrammatic top view illustration of the embodiment shown in FIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments may include a vehicle 200 including in its body a system 100 with a first image capture device 122 positioned in the vicinity of the rearview mirror and/or near the driver of vehicle 200, a second image capture device 124 positioned on or in a bumper region (e.g., one of bumper regions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both be positioned in the vicinity of the rearview mirror and/or near the driver of vehicle 200. Additionally, while two image capture devices 122 and 124 are shown in FIGS. 2B and 2C, it should be understood that other embodiments may include more than two image capture devices. For example, in the embodiments shown in FIGS. 2D and 2E, first, second, and third image capture devices 122, 124, and 126, are included in the system 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned in the vicinity of the rearview mirror and/or near the driver of vehicle 200, and image capture devices 124 and 126 may be positioned on or in a bumper region (e.g., one of bumper regions 210) of vehicle 200. And as shown in FIG. 2E, image capture devices 122, 124, and 126 may be positioned in the vicinity of the rearview mirror and/or near the driver seat of vehicle 200. The disclosed embodiments are not limited to any particular number and configuration of the image capture devices, and the image capture devices may be positioned in any appropriate location within and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited to vehicles and could be applied in other contexts. It is also to be understood that disclosed embodiments are not limited to a particular type of vehicle 200 and may be applicable to all types of vehicles including automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type of image capture device. Image capture device 122 may include an optical axis. In one instance, the image capture device 122 may include an Aptina M9V024 WVGA sensor with a global shutter. In other embodiments, image capture device 122 may provide a resolution of 1280×960 pixels and may include a rolling shutter. Image capture device 122 may include various optical elements. In some embodiments one or more lenses may be included, for example, to provide a desired focal length and field of view for the image capture device. In some embodiments, image capture device 122 may be associated with a 6 mm lens or a 12 mm lens. In some embodiments, image capture device 122 may be configured to capture images having a desired field-of-view (FOV) 202, as illustrated in FIG. 2D. For example, image capture device 122 may be configured to have a regular FOV, such as within a range of 40 degrees to 56 degrees, including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater. Alternatively, image capture device 122 may be configured to have a narrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or 36 degree FOV. In addition, image capture device 122 may be configured to have a wide FOV in the range of 100 to 180 degrees. In some embodiments, image capture device 122 may include a wide angle bumper camera or one with up to a 180 degree FOV. In some embodiments, image capture device 122 may be a 7.2M pixel image capture device with an aspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) with about 100 degree horizontal FOV. Such an image capture device may be used in place of a three image capture device configuration. Due to significant lens distortion, the vertical FOV of such an image capture device may be significantly less than 50 degrees in implementations in which the image capture device uses a radially symmetric lens. For example, such a lens may not be radially symmetric which would allow for a vertical FOV greater than 50 degrees with 100 degree horizontal FOV.

The first image capture device 122 may acquire a plurality of first images relative to a scene associated with the vehicle 200. Each of the plurality of first images may be acquired as a series of image scan lines, which may be captured using a rolling shutter. Each scan line may include a plurality of pixels.

The first image capture device 122 may have a scan rate associated with acquisition of each of the first series of image scan lines. The scan rate may refer to a rate at which an image sensor can acquire image data associated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable type and number of image sensors, including CCD sensors or CMOS sensors, for example. In one embodiment, a CMOS image sensor may be employed along with a rolling shutter, such that each pixel in a row is read one at a time, and scanning of the rows proceeds on a row-by-row basis until an entire image frame has been captured. In some embodiments, the rows may be captured sequentially from top to bottom relative to the frame.

In some embodiments, one or more of the image capture devices (e.g., image capture devices 122, 124, and 126) disclosed herein may constitute a high resolution imager and may have a resolution greater than 5M pixel, 7M pixel, 10M pixel, or greater.

The use of a rolling shutter may result in pixels in different rows being exposed and captured at different times, which may cause skew and other image artifacts in the captured image frame. On the other hand, when the image capture device 122 is configured to operate with a global or synchronous shutter, all of the pixels may be exposed for the same amount of time and during a common exposure period. As a result, the image data in a frame collected from a system employing a global shutter represents a snapshot of the entire FOV (such as FOV 202) at a particular time. In contrast, in a rolling shutter application, each row in a frame is exposed and data is capture at different times. Thus, moving objects may appear distorted in an image capture device having a rolling shutter. This phenomenon will be described in greater detail below.

The second image capture device 124 and the third image capturing device 126 may be any type of image capture device. Like the first image capture device 122, each of image capture devices 124 and 126 may include an optical axis. In one embodiment, each of image capture devices 124 and 126 may include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of image capture devices 124 and 126 may include a rolling shutter. Like image capture device 122, image capture devices 124 and 126 may be configured to include various lenses and optical elements. In some embodiments, lenses associated with image capture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206) that are the same as, or narrower than, a FOV (such as FOV 202) associated with image capture device 122. For example, image capture devices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second and third images relative to a scene associated with the vehicle 200. Each of the plurality of second and third images may be acquired as a second and third series of image scan lines, which may be captured using a rolling shutter. Each scan line or row may have a plurality of pixels. Image capture devices 124 and 126 may have second and third scan rates associated with acquisition of each of image scan lines included in the second and third series.

Each image capture device 122, 124, and 126 may be positioned at any suitable position and orientation relative to vehicle 200. The relative positioning of the image capture devices 122, 124, and 126 may be selected to aid in fusing together the information acquired from the image capture devices. For example, in some embodiments, a FOV (such as FOV 204) associated with image capture device 124 may overlap partially or fully with a FOV (such as FOV 202) associated with image capture device 122 and a FOV (such as FOV 206) associated with image capture device 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 at any suitable relative heights. In one instance, there may be a height difference between the image capture devices 122, 124, and 126, which may provide sufficient parallax information to enable stereo analysis. For example, as shown in FIG. 2A, the two image capture devices 122 and 124 are at different heights. There may also be a lateral displacement difference between image capture devices 122, 124, and 126, giving additional parallax information for stereo analysis by processing unit 110, for example. The difference in the lateral displacement may be denoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, fore or aft displacement (e.g., range displacement) may exist between image capture devices 122, 124, and 126. For example, image capture device 122 may be located 0.5 to 2 meters or more behind image capture device 124 and/or image capture device 126. This type of displacement may enable one of the image capture devices to cover potential blind spots of the other image capture device(s).

Image capture devices 122 may have any suitable resolution capability (e.g., number of pixels associated with the image sensor), and the resolution of the image sensor(s) associated with the image capture device 122 may be higher, lower, or the same as the resolution of the image sensor(s) associated with image capture devices 124 and 126. In some embodiments, the image sensor(s) associated with image capture device 122 and/or image capture devices 124 and 126 may have a resolution of 640×480, 1024×768, 1280×960, or any other suitable resolution.

The frame rate (e.g., the rate at which an image capture device acquires a set of pixel data of one image frame before moving on to capture pixel data associated with the next image frame) may be controllable. The frame rate associated with image capture device 122 may be higher, lower, or the same as the frame rate associated with image capture devices 124 and 126. The frame rate associated with image capture devices 122, 124, and 126 may depend on a variety of factors that may affect the timing of the frame rate. For example, one or more of image capture devices 122, 124, and 126 may include a selectable pixel delay period imposed before or after acquisition of image data associated with one or more pixels of an image sensor in image capture device 122, 124, and/or 126. Generally, image data corresponding to each pixel may be acquired according to a clock rate for the device (e.g., one pixel per clock cycle). Additionally, in embodiments including a rolling shutter, one or more of image capture devices 122, 124, and 126 may include a selectable horizontal blanking period imposed before or after acquisition of image data associated with a row of pixels of an image sensor in image capture device 122, 124, and/or 126. Further, one or more of image capture devices 122, 124, and/or 126 may include a selectable vertical blanking period imposed before or after acquisition of image data associated with an image frame of image capture device 122, 124, and 126.

These timing controls may enable synchronization of frame rates associated with image capture devices 122, 124, and 126, even where the line scan rates of each are different. Additionally, as will be discussed in greater detail below, these selectable timing controls, among other factors (e.g., image sensor resolution, maximum line scan rates, etc.) may enable synchronization of image capture from an area where the FOV of image capture device 122 overlaps with one or more FOVs of image capture devices 124 and 126, even where the field of view of image capture device 122 is different from the FOVs of image capture devices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may depend on the resolution of the associated image sensors. For example, assuming similar line scan rates for both devices, if one device includes an image sensor having a resolution of 640×480 and another device includes an image sensor with a resolution of 1280×960, then more time will be required to acquire a frame of image data from the sensor having the higher resolution.

Another factor that may affect the timing of image data acquisition in image capture devices 122, 124, and 126 is the maximum line scan rate. For example, acquisition of a row of image data from an image sensor included in image capture device 122, 124, and 126 will require some minimum amount of time. Assuming no pixel delay periods are added, this minimum amount of time for acquisition of a row of image data will be related to the maximum line scan rate for a particular device. Devices that offer higher maximum line scan rates have the potential to provide higher frame rates than devices with lower maximum line scan rates. In some embodiments, one or more of image capture devices 124 and 126 may have a maximum line scan rate that is higher than a maximum line scan rate associated with image capture device 122. In some embodiments, the maximum line scan rate of image capture device 124 and/or 126 may be 1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate of image capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may have the same maximum line scan rate, but image capture device 122 may be operated at a scan rate less than or equal to its maximum scan rate. The system may be configured such that one or more of image capture devices 124 and 126 operate at a line scan rate that is equal to the line scan rate of image capture device 122. In other instances, the system may be configured such that the line scan rate of image capture device 124 and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times or more than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may be asymmetric. That is, they may include cameras having different fields of view (FOV) and focal lengths. The fields of view of image capture devices 122, 124, and 126 may include any desired area relative to an environment of vehicle 200, for example. In some embodiments, one or more of image capture devices 122, 124, and 126 may be configured to acquire image data from an environment in front of vehicle 200, behind vehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122, 124, and/or 126 may be selectable (e.g., by inclusion of appropriate lenses etc.) such that each device acquires images of objects at a desired distance range relative to vehicle 200. For example, in some embodiments image capture devices 122, 124, and 126 may acquire images of close-up objects within a few meters from the vehicle. Image capture devices 122, 124, and 126 may also be configured to acquire images of objects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100 m, 150 m, or more). Further, the focal lengths of image capture devices 122, 124, and 126 may be selected such that one image capture device (e.g., image capture device 122) can acquire images of objects relatively close to the vehicle (e.g., within 10 m or within 20 m) while the other image capture devices (e.g., image capture devices 124 and 126) can acquire images of more distant objects (e.g., greater than 20 m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capture devices 122, 124, and 126 may have a wide angle. For example, it may be advantageous to have a FOV of 140 degrees, especially for image capture devices 122, 124, and 126 that may be used to capture images of the area in the vicinity of vehicle 200. For example, image capture device 122 may be used to capture images of the area to the right or left of vehicle 200 and, in such embodiments, it may be desirable for image capture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122, 124, and 126 may depend on the respective focal lengths. For example, as the focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have any suitable fields of view. In one particular example, image capture device 122 may have a horizontal FOV of 46 degrees, image capture device 124 may have a horizontal FOV of 23 degrees, and image capture device 126 may have a horizontal FOV in between 23 and 46 degrees. In another instance, image capture device 122 may have a horizontal FOV of 52 degrees, image capture device 124 may have a horizontal FOV of 26 degrees, and image capture device 126 may have a horizontal FOV in between 26 and 52 degrees. In some embodiments, a ratio of the FOV of image capture device 122 to the FOVs of image capture device 124 and/or image capture device 126 may vary from 1.5 to 2.0. In other embodiments, this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capture device 122 overlaps, at least partially or fully, with a field of view of image capture device 124 and/or image capture device 126. In some embodiments, system 100 may be configured such that the fields of view of image capture devices 124 and 126, for example, fall within (e.g., are narrower than) and share a common center with the field of view of image capture device 122. In other embodiments, the image capture devices 122, 124, and 126 may capture adjacent FOVs or may have partial overlap in their FOVs. In some embodiments, the fields of view of image capture devices 122, 124, and 126 may be aligned such that a center of the narrower FOV image capture devices 124 and/or 126 may be located in a lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle control systems, consistent with the disclosed embodiments. As indicated in FIG. 2F, vehicle 200 may include throttling system 220, braking system 230, and steering system 240. System 100 may provide inputs (e.g., control signals) to one or more of throttling system 220, braking system 230, and steering system 240 over one or more data links (e.g., any wired and/or wireless link or links for transmitting data). For example, based on analysis of images acquired by image capture devices 122, 124, and/or 126, system 100 may provide control signals to one or more of throttling system 220, braking system 230, and steering system 240 to navigate vehicle 200 (e.g., by causing an acceleration, a turn, a lane shift, etc.). Further, system 100 may receive inputs from one or more of throttling system 220, braking system 230, and steering system 24 indicating operating conditions of vehicle 200 (e.g., speed, whether vehicle 200 is braking and/or turning, etc.). Further details are provided in connection with FIGS. 4-7 , below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170 for interacting with a driver or a passenger of vehicle 200. For example, user interface 170 in a vehicle application may include a touch screen 320, knobs 330, buttons 340, and a microphone 350. A driver or passenger of vehicle 200 may also use handles (e.g., located on or near the steering column of vehicle 200 including, for example, turn signal handles), buttons (e.g., located on the steering wheel of vehicle 200), and the like, to interact with system 100. In some embodiments, microphone 350 may be positioned adjacent to a rearview mirror 310. Similarly, in some embodiments, image capture device 122 may be located near rearview mirror 310. In some embodiments, user interface 170 may also include one or more speakers 360 (e.g., speakers of a vehicle audio system). For example, system 100 may provide various notifications (e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370 configured to be positioned behind a rearview mirror (e.g., rearview mirror 310) and against a vehicle windshield, consistent with disclosed embodiments. As shown in FIG. 3B, camera mount 370 may include image capture devices 122, 124, and 126. Image capture devices 124 and 126 may be positioned behind a glare shield 380, which may be flush against the vehicle windshield and include a composition of film and/or anti-reflective materials. For example, glare shield 380 may be positioned such that the shield aligns against a vehicle windshield having a matching slope. In some embodiments, each of image capture devices 122, 124, and 126 may be positioned behind glare shield 380, as depicted, for example, in FIG. 3D. The disclosed embodiments are not limited to any particular configuration of image capture devices 122, 124, and 126, camera mount 370, and glare shield 380. FIG. 3C is an illustration of camera mount 370 shown in FIG. 3B from a front perspective.

As will be appreciated by a person skilled in the art having the benefit of this disclosure, numerous variations and/or modifications may be made to the foregoing disclosed embodiments. For example, not all components are essential for the operation of system 100. Further, any component may be located in any appropriate part of system 100 and the components may be rearranged into a variety of configurations while providing the functionality of the disclosed embodiments. Therefore, the foregoing configurations are examples and, regardless of the configurations discussed above, system 100 can provide a wide range of functionality to analyze the surroundings of vehicle 200 and navigate vehicle 200 in response to the analysis.

As discussed below in further detail and consistent with various disclosed embodiments, system 100 may provide a variety of features related to autonomous driving and/or driver assist technology. For example, system 100 may analyze image data, position data (e.g., GPS location information), map data, speed data, and/or data from sensors included in vehicle 200. System 100 may collect the data for analysis from, for example, image acquisition unit 120, position sensor 130, and other sensors. Further, system 100 may analyze the collected data to determine whether or not vehicle 200 should take a certain action, and then automatically take the determined action without human intervention. For example, when vehicle 200 navigates without human intervention, system 100 may automatically control the braking, acceleration, and/or steering of vehicle 200 (e.g., by sending control signals to one or more of throttling system 220, braking system 230, and steering system 240). Further, system 100 may analyze the collected data and issue warnings and/or alerts to vehicle occupants based on the analysis of the collected data. Additional details regarding the various embodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionality that uses a multi-camera system. The multi-camera system may use one or more cameras facing in the forward direction of a vehicle. In other embodiments, the multi-camera system may include one or more cameras facing to the side of a vehicle or to the rear of the vehicle. In one embodiment, for example, system 100 may use a two-camera imaging system, where a first camera and a second camera (e.g., image capture devices 122 and 124) may be positioned at the front and/or the sides of a vehicle (e.g., vehicle 200). The first camera may have a field of view that is greater than, less than, or partially overlapping with, the field of view of the second camera. In addition, the first camera may be connected to a first image processor to perform monocular image analysis of images provided by the first camera, and the second camera may be connected to a second image processor to perform monocular image analysis of images provided by the second camera. The outputs (e.g., processed information) of the first and second image processors may be combined. In some embodiments, the second image processor may receive images from both the first camera and second camera to perform stereo analysis. In another embodiment, system 100 may use a three-camera imaging system where each of the cameras has a different field of view. Such a system may, therefore, make decisions based on information derived from objects located at varying distances both forward and to the sides of the vehicle. References to monocular image analysis may refer to instances where image analysis is performed based on images captured from a single point of view (e.g., from a single camera). Stereo image analysis may refer to instances where image analysis is performed based on two or more images captured with one or more variations of an image capture parameter. For example, captured images suitable for performing stereo image analysis may include images captured: from two or more different positions, from different fields of view, using different focal lengths, along with parallax information, etc.

For example, in one embodiment, system 100 may implement a three camera configuration using image capture devices 122, 124, and 126. In such a configuration, image capture device 122 may provide a narrow field of view (e.g., 34 degrees, or other values selected from a range of about 20 to 45 degrees, etc.), image capture device 124 may provide a wide field of view (e.g., 150 degrees or other values selected from a range of about 100 to about 180 degrees), and image capture device 126 may provide an intermediate field of view (e.g., 46 degrees or other values selected from a range of about 35 to about 60 degrees). In some embodiments, image capture device 126 may act as a main or primary camera. Image capture devices 122, 124, and 126 may be positioned behind rearview mirror 310 and positioned substantially side-by-side (e.g., 6 cm apart). Further, in some embodiments, as discussed above, one or more of image capture devices 122, 124, and 126 may be mounted behind glare shield 380 that is flush with the windshield of vehicle 200. Such shielding may act to minimize the impact of any reflections from inside the car on image capture devices 122, 124, and 126.

In another embodiment, as discussed above in connection with FIGS. 3B and 3C, the wide field of view camera (e.g., image capture device 124 in the above example) may be mounted lower than the narrow and main field of view cameras (e.g., image devices 122 and 126 in the above example). This configuration may provide a free line of sight from the wide field of view camera. To reduce reflections, the cameras may be mounted close to the windshield of vehicle 200, and may include polarizers on the cameras to damp reflected light.

A three camera system may provide certain performance characteristics. For example, some embodiments may include an ability to validate the detection of objects by one camera based on detection results from another camera. In the three camera configuration discussed above, processing unit 110 may include, for example, three processing devices (e.g., three EyeQ series of processor chips, as discussed above), with each processing device dedicated to processing images captured by one or more of image capture devices 122, 124, and 126.

In a three camera system, a first processing device may receive images from both the main camera and the narrow field of view camera, and perform vision processing of the narrow FOV camera to, for example, detect other vehicles, pedestrians, lane marks, traffic signs, traffic lights, and other road objects. Further, the first processing device may calculate a disparity of pixels between the images from the main camera and the narrow camera and create a 3D reconstruction of the environment of vehicle 200. The first processing device may then combine the 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera.

The second processing device may receive images from main camera and perform vision processing to detect other vehicles, pedestrians, lane marks, traffic signs, traffic lights, and other road objects. Additionally, the second processing device may calculate a camera displacement and, based on the displacement, calculate a disparity of pixels between successive images and create a 3D reconstruction of the scene (e.g., a structure from motion). The second processing device may send the structure from motion based 3D reconstruction to the first processing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV camera and process the images to detect vehicles, pedestrians, lane marks, traffic signs, traffic lights, and other road objects. The third processing device may further execute additional processing instructions to analyze images to identify objects moving in the image, such as vehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information captured and processed independently may provide an opportunity for providing redundancy in the system. Such redundancy may include, for example, using a first image capture device and the images processed from that device to validate and/or supplement information obtained by capturing and processing image information from at least a second image capture device.

In some embodiments, system 100 may use two image capture devices (e.g., image capture devices 122 and 124) in providing navigation assistance for vehicle 200 and use a third image capture device (e.g., image capture device 126) to provide redundancy and validate the analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices 122 and 124 may provide images for stereo analysis by system 100 for navigating vehicle 200, while image capture device 126 may provide images for monocular analysis by system 100 to provide redundancy and validation of information obtained based on images captured from image capture device 122 and/or image capture device 124. That is, image capture device 126 (and a corresponding processing device) may be considered to provide a redundant sub-system for providing a check on the analysis derived from image capture devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). Furthermore, in some embodiments, redundancy and validation of received data may be supplemented based on information received from one more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers outside of a vehicle, etc.).

One of skill in the art will recognize that the above camera configurations, camera placements, number of cameras, camera locations, etc., are examples only. These components and others described relative to the overall system may be assembled and used in a variety of different configurations without departing from the scope of the disclosed embodiments. Further details regarding usage of a multi-camera system to provide driver assist and/or autonomous vehicle functionality follow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or 150, which may be stored/programmed with instructions for performing one or more operations consistent with the disclosed embodiments. Although the following refers to memory 140, one of skill in the art will recognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4 , memory 140 may store a monocular image analysis module 402, a stereo image analysis module 404, a velocity and acceleration module 406, and a navigational response module 408. The disclosed embodiments are not limited to any particular configuration of memory 140. Further, application processor 180 and/or image processor 190 may execute the instructions stored in any of modules 402, 404, 406, and 408 included in memory 140. One of skill in the art will understand that references in the following discussions to processing unit 110 may refer to application processor 180 and image processor 190 individually or collectively. Accordingly, steps of any of the following processes may be performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may store instructions (such as computer vision software) which, when executed by processing unit 110, performs monocular image analysis of a set of images acquired by one of image capture devices 122, 124, and 126. In some embodiments, processing unit 110 may combine information from a set of images with additional sensory information (e.g., information from radar, lidar, etc.) to perform the monocular image analysis. As described in connection with FIGS. 5A-5D below, monocular image analysis module 402 may include instructions for detecting a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and any other feature associated with an environment of a vehicle. Based on the analysis, system 100 (e.g., via processing unit 110) may cause one or more navigational responses in vehicle 200, such as a turn, a lane shift, a change in acceleration, and the like, as discussed below in connection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may store instructions (such as computer vision software) which, when executed by processing unit 110, performs stereo image analysis of first and second sets of images acquired by a combination of image capture devices selected from any of image capture devices 122, 124, and 126. In some embodiments, processing unit 110 may combine information from the first and second sets of images with additional sensory information (e.g., information from radar) to perform the stereo image analysis. For example, stereo image analysis module 404 may include instructions for performing stereo image analysis based on a first set of images acquired by image capture device 124 and a second set of images acquired by image capture device 126. As described in connection with FIG. 6 below, stereo image analysis module 404 may include instructions for detecting a set of features within the first and second sets of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and the like. Based on the analysis, processing unit 110 may cause one or more navigational responses in vehicle 200, such as a turn, a lane shift, a change in acceleration, and the like, as discussed below in connection with navigational response module 408. Furthermore, in some embodiments, stereo image analysis module 404 may implement techniques associated with a trained system (such as a neural network or a deep neural network) or an untrained system, such as a system that may be configured to use computer vision algorithms to detect and/or label objects in an environment from which sensory information was captured and processed. In one embodiment, stereo image analysis module 404 and/or other image processing modules may be configured to use a combination of a trained and untrained system.

In one embodiment, velocity and acceleration module 406 may store software configured to analyze data received from one or more computing and electromechanical devices in vehicle 200 that are configured to cause a change in velocity and/or acceleration of vehicle 200. For example, processing unit 110 may execute instructions associated with velocity and acceleration module 406 to calculate a target speed for vehicle 200 based on data derived from execution of monocular image analysis module 402 and/or stereo image analysis module 404. Such data may include, for example, a target position, velocity, and/or acceleration, the position and/or speed of vehicle 200 relative to a nearby vehicle, pedestrian, or road object, position information for vehicle 200 relative to lane markings of the road, and the like. In addition, processing unit 110 may calculate a target speed for vehicle 200 based on sensory input (e.g., information from radar) and input from other systems of vehicle 200, such as throttling system 220, braking system 230, and/or steering system 240 of vehicle 200. Based on the calculated target speed, processing unit 110 may transmit electronic signals to throttling system 220, braking system 230, and/or steering system 240 of vehicle 200 to trigger a change in velocity and/or acceleration by, for example, physically depressing the brake or easing up off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store software executable by processing unit 110 to determine a desired navigational response based on data derived from execution of monocular image analysis module 402 and/or stereo image analysis module 404. Such data may include position and speed information associated with nearby vehicles, pedestrians, and road objects, target position information for vehicle 200, and the like. Additionally, in some embodiments, the navigational response may be based (partially or fully) on map data, a predetermined position of vehicle 200, and/or a relative velocity or a relative acceleration between vehicle 200 and one or more objects detected from execution of monocular image analysis module 402 and/or stereo image analysis module 404. Navigational response module 408 may also determine a desired navigational response based on sensory input (e.g., information from radar) and inputs from other systems of vehicle 200, such as throttling system 220, braking system 230, and steering system 240 of vehicle 200. Based on the desired navigational response, processing unit 110 may transmit electronic signals to throttling system 220, braking system 230, and steering system 240 of vehicle 200 to trigger a desired navigational response by, for example, turning the steering wheel of vehicle 200 to achieve a rotation of a predetermined angle. In some embodiments, processing unit 110 may use the output of navigational response module 408 (e.g., the desired navigational response) as an input to execution of velocity and acceleration module 406 for calculating a change in speed of vehicle 200.

Furthermore, any of the modules (e.g., modules 402, 404, and 406) disclosed herein may implement techniques associated with a trained system (such as a neural network or a deep neural network) or an untrained system.

FIG. 5A is a flowchart showing an exemplary process 500A for causing one or more navigational responses based on monocular image analysis, consistent with disclosed embodiments. At step 510, processing unit 110 may receive a plurality of images via data interface 128 between processing unit 110 and image acquisition unit 120. For instance, a camera included in image acquisition unit 120 (such as image capture device 122 having field of view 202) may capture a plurality of images of an area forward of vehicle 200 (or to the sides or rear of a vehicle, for example) and transmit them over a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.) to processing unit 110. Processing unit 110 may execute monocular image analysis module 402 to analyze the plurality of images at step 520, as described in further detail in connection with FIGS. 5B-5D below. By performing the analysis, processing unit 110 may detect a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402 to detect various road hazards at step 520, such as, for example, parts of a truck tire, fallen road signs, loose cargo, small animals, and the like. Road hazards may vary in structure, shape, size, and color, which may make detection of such hazards more challenging. In some embodiments, processing unit 110 may execute monocular image analysis module 402 to perform multi-frame analysis on the plurality of images to detect road hazards. For example, processing unit 110 may estimate camera motion between consecutive image frames and calculate the disparities in pixels between the frames to construct a 3D-map of the road. Processing unit 110 may then use the 3D-map to detect the road surface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational response module 408 to cause one or more navigational responses in vehicle 200 based on the analysis performed at step 520 and the techniques as described above in connection with FIG. 4 . Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. In some embodiments, processing unit 110 may use data derived from execution of velocity and acceleration module 406 to cause the one or more navigational responses. Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof. For instance, processing unit 110 may cause vehicle 200 to shift one lane over and then accelerate by, for example, sequentially transmitting control signals to steering system 240 and throttling system 220 of vehicle 200. Alternatively, processing unit 110 may cause vehicle 200 to brake while at the same time shifting lanes by, for example, simultaneously transmitting control signals to braking system 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detecting one or more vehicles and/or pedestrians in a set of images, consistent with disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500B. At step 540, processing unit 110 may determine a set of candidate objects representing possible vehicles and/or pedestrians. For example, processing unit 110 may scan one or more images, compare the images to one or more predetermined patterns, and identify within each image possible locations that may contain objects of interest (e.g., vehicles, pedestrians, or portions thereof). The predetermined patterns may be designed in such a way to achieve a high rate of “false hits” and a low rate of “misses.” For example, processing unit 110 may use a low threshold of similarity to predetermined patterns for identifying candidate objects as possible vehicles or pedestrians. Doing so may allow processing unit 110 to reduce the probability of missing (e.g., not identifying) a candidate object representing a vehicle or pedestrian.

At step 542, processing unit 110 may filter the set of candidate objects to exclude certain candidates (e.g., irrelevant or less relevant objects) based on classification criteria. Such criteria may be derived from various properties associated with object types stored in a database (e.g., a database stored in memory 140). Properties may include object shape, dimensions, texture, position (e.g., relative to vehicle 200), and the like. Thus, processing unit 110 may use one or more sets of criteria to reject false candidates from the set of candidate objects.

At step 544, processing unit 110 may analyze multiple frames of images to determine whether objects in the set of candidate objects represent vehicles and/or pedestrians. For example, processing unit 110 may track a detected candidate object across consecutive frames and accumulate frame-by-frame data associated with the detected object (e.g., size, position relative to vehicle 200, etc.). Additionally, processing unit 110 may estimate parameters for the detected object and compare the object's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values (relative to vehicle 200) associated with the detected objects. In some embodiments, processing unit 110 may construct the measurements based on estimation techniques using a series of time-based observations such as Kalman filters or linear quadratic estimation (LQE), and/or based on available modeling data for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). The Kalman filters may be based on a measurement of an object's scale, where the scale measurement is proportional to a time to collision (e.g., the amount of time for vehicle 200 to reach the object). Thus, by performing steps 540-546, processing unit 110 may identify vehicles and pedestrians appearing within the set of captured images and derive information (e.g., position, speed, size) associated with the vehicles and pedestrians. Based on the identification and the derived information, processing unit 110 may cause one or more navigational responses in vehicle 200, as described in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis of one or more images to reduce the probabilities of detecting a “false hit” and missing a candidate object that represents a vehicle or pedestrian. The optical flow analysis may refer to, for example, analyzing motion patterns relative to vehicle 200 in the one or more images associated with other vehicles and pedestrians, and that are distinct from road surface motion. Processing unit 110 may calculate the motion of candidate objects by observing the different positions of the objects across multiple image frames, which are captured at different times. Processing unit 110 may use the position and time values as inputs into mathematical models for calculating the motion of the candidate objects. Thus, optical flow analysis may provide another method of detecting vehicles and pedestrians that are nearby vehicle 200. Processing unit 110 may perform optical flow analysis in combination with steps 540-546 to provide redundancy for detecting vehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detecting road marks and/or lane geometry information in a set of images, consistent with disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500C. At step 550, processing unit 110 may detect a set of objects by scanning one or more images. To detect segments of lane markings, lane geometry information, and other pertinent road marks, processing unit 110 may filter the set of objects to exclude those determined to be irrelevant (e.g., minor potholes, small rocks, etc.). At step 552, processing unit 110 may group together the segments detected in step 550 belonging to the same road mark or lane mark. Based on the grouping, processing unit 110 may develop a model to represent the detected segments, such as a mathematical model.

At step 554, processing unit 110 may construct a set of measurements associated with the detected segments. In some embodiments, processing unit 110 may create a projection of the detected segments from the image plane onto the real-world plane. The projection may be characterized using a 3rd-degree polynomial having coefficients corresponding to physical properties such as the position, slope, curvature, and curvature derivative of the detected road. In generating the projection, processing unit 110 may take into account changes in the road surface, as well as pitch and roll rates associated with vehicle 200. In addition, processing unit 110 may model the road elevation by analyzing position and motion cues present on the road surface. Further, processing unit 110 may estimate the pitch and roll rates associated with vehicle 200 by tracking a set of feature points in the one or more images.

At step 556, processing unit 110 may perform multi-frame analysis by, for example, tracking the detected segments across consecutive image frames and accumulating frame-by-frame data associated with detected segments. As processing unit 110 performs multi-frame analysis, the set of measurements constructed at step 554 may become more reliable and associated with an increasingly higher confidence level. Thus, by performing steps 550, 552, 554, and 556, processing unit 110 may identify road marks appearing within the set of captured images and derive lane geometry information. Based on the identification and the derived information, processing unit 110 may cause one or more navigational responses in vehicle 200, as described in connection with FIG. 5A, above.

At step 558, processing unit 110 may consider additional sources of information to further develop a safety model for vehicle 200 in the context of its surroundings. Processing unit 110 may use the safety model to define a context in which system 100 may execute autonomous control of vehicle 200 in a safe manner. To develop the safety model, in some embodiments, processing unit 110 may consider the position and motion of other vehicles, the detected road edges and barriers, and/or general road shape descriptions extracted from map data (such as data from map database 160). By considering additional sources of information, processing unit 110 may provide redundancy for detecting road marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detecting traffic lights in a set of images, consistent with disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500D. At step 560, processing unit 110 may scan the set of images and identify objects appearing at locations in the images likely to contain traffic lights. For example, processing unit 110 may filter the identified objects to construct a set of candidate objects, excluding those objects unlikely to correspond to traffic lights. The filtering may be done based on various properties associated with traffic lights, such as shape, dimensions, texture, position (e.g., relative to vehicle 200), and the like. Such properties may be based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, processing unit 110 may perform multi-frame analysis on the set of candidate objects reflecting possible traffic lights. For example, processing unit 110 may track the candidate objects across consecutive image frames, estimate the real-world position of the candidate objects, and filter out those objects that are moving (which are unlikely to be traffic lights). In some embodiments, processing unit 110 may perform color analysis on the candidate objects and identify the relative position of the detected colors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction. The analysis may be based on any combination of: (i) the number of lanes detected on either side of vehicle 200, (ii) markings (such as arrow marks) detected on the road, and (iii) descriptions of the junction extracted from map data (such as data from map database 160). Processing unit 110 may conduct the analysis using information derived from execution of monocular analysis module 402. In addition, Processing unit 110 may determine a correspondence between the traffic lights detected at step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110 may update the confidence level associated with the analyzed junction geometry and the detected traffic lights. For instance, the number of traffic lights estimated to appear at the junction as compared with the number actually appearing at the junction may impact the confidence level. Thus, based on the confidence level, processing unit 110 may delegate control to the driver of vehicle 200 in order to improve safety conditions. By performing steps 560, 562, and 564, processing unit 110 may identify traffic lights appearing within the set of captured images and analyze junction geometry information. Based on the identification and the analysis, processing unit 110 may cause one or more navigational responses in vehicle 200, as described in connection with FIG. 5A, above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing one or more navigational responses in vehicle 200 based on a vehicle path, consistent with the disclosed embodiments. At step 570, processing unit 110 may construct an initial vehicle path associated with vehicle 200. The vehicle path may be represented using a set of points expressed in coordinates (x, z), and the distance d_(i) between two points in the set of points may fall in the range of 1 to 5 meters. In one embodiment, processing unit 110 may construct the initial vehicle path using two polynomials, such as left and right road polynomials. Processing unit 110 may calculate the geometric midpoint between the two polynomials and offset each point included in the resultant vehicle path by a predetermined offset (e.g., a smart lane offset), if any (an offset of zero may correspond to travel in the middle of a lane). The offset may be in a direction perpendicular to a segment between any two points in the vehicle path. In another embodiment, processing unit 110 may use one polynomial and an estimated lane width to offset each point of the vehicle path by half the estimated lane width plus a predetermined offset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructed at step 570. Processing unit 110 may reconstruct the vehicle path constructed at step 570 using a higher resolution, such that the distance d_(k) between two points in the set of points representing the vehicle path is less than the distance d_(i) described above. For example, the distance d_(k) may fall in the range of 0.1 to 0.3 meters. Processing unit 110 may reconstruct the vehicle path using a parabolic spline algorithm, which may yield a cumulative distance vector S corresponding to the total length of the vehicle path (i.e., based on the set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point (expressed in coordinates as (x_(l), z_(l))) based on the updated vehicle path constructed at step 572. Processing unit 110 may extract the look-ahead point from the cumulative distance vector S, and the look-ahead point may be associated with a look-ahead distance and look-ahead time. The look-ahead distance, which may have a lower bound ranging from 10 to 20 meters, may be calculated as the product of the speed of vehicle 200 and the look-ahead time. For example, as the speed of vehicle 200 decreases, the look-ahead distance may also decrease (e.g., until it reaches the lower bound). The look-ahead time, which may range from 0.5 to 1.5 seconds, may be inversely proportional to the gain of one or more control loops associated with causing a navigational response in vehicle 200, such as the heading error tracking control loop. For example, the gain of the heading error tracking control loop may depend on the bandwidth of a yaw rate loop, a steering actuator loop, car lateral dynamics, and the like. Thus, the higher the gain of the heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yaw rate command based on the look-ahead point determined at step 574. Processing unit 110 may determine the heading error by calculating the arctangent of the look-ahead point, e.g., arctan (x_(l)/z_(l)). Processing unit 110 may determine the yaw rate command as the product of the heading error and a high-level control gain. The high-level control gain may be equal to: (2/look-ahead time), if the look-ahead distance is not at the lower bound. Otherwise, the high-level control gain may be equal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determining whether a leading vehicle is changing lanes, consistent with the disclosed embodiments. At step 580, processing unit 110 may determine navigation information associated with a leading vehicle (e.g., a vehicle traveling ahead of vehicle 200). For example, processing unit 110 may determine the position, velocity (e.g., direction and speed), and/or acceleration of the leading vehicle, using the techniques described in connection with FIGS. 5A and 5B, above. Processing unit 110 may also determine one or more road polynomials, a look-ahead point (associated with vehicle 200), and/or a snail trail (e.g., a set of points describing a path taken by the leading vehicle), using the techniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation information determined at step 580. In one embodiment, processing unit 110 may calculate the distance between a snail trail and a road polynomial (e.g., along the trail). If the variance of this distance along the trail exceeds a predetermined threshold (for example, 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on a moderately curvy road, and 0.5 to 0.6 meters on a road with sharp curves), processing unit 110 may determine that the leading vehicle is likely changing lanes. In the case where multiple vehicles are detected traveling ahead of vehicle 200, processing unit 110 may compare the snail trails associated with each vehicle. Based on the comparison, processing unit 110 may determine that a vehicle whose snail trail does not match with the snail trails of the other vehicles is likely changing lanes. Processing unit 110 may additionally compare the curvature of the snail trail (associated with the leading vehicle) with the expected curvature of the road segment in which the leading vehicle is traveling. The expected curvature may be extracted from map data (e.g., data from map database 160), from road polynomials, from other vehicles' snail trails, from prior knowledge about the road, and the like. If the difference in curvature of the snail trail and the expected curvature of the road segment exceeds a predetermined threshold, processing unit 110 may determine that the leading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leading vehicle's instantaneous position with the look-ahead point (associated with vehicle 200) over a specific period of time (e.g., 0.5 to 1.5 seconds). If the distance between the leading vehicle's instantaneous position and the look-ahead point varies during the specific period of time, and the cumulative sum of variation exceeds a predetermined threshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curvy road, and 1.3 to 1.7 meters on a road with sharp curves), processing unit 110 may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit 110 may analyze the geometry of the snail trail by comparing the lateral distance traveled along the trail with the expected curvature of the snail trail. The expected radius of curvature may be determined according to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x) represents the lateral distance traveled and δ_(z) represents the longitudinal distance traveled. If the difference between the lateral distance traveled and the expected curvature exceeds a predetermined threshold (e.g., 500 to 700 meters), processing unit 110 may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit 110 may analyze the position of the leading vehicle. If the position of the leading vehicle obscures a road polynomial (e.g., the leading vehicle is overlaid on top of the road polynomial), then processing unit 110 may determine that the leading vehicle is likely changing lanes. In the case where the position of the leading vehicle is such that, another vehicle is detected ahead of the leading vehicle and the snail trails of the two vehicles are not parallel, processing unit 110 may determine that the (closer) leading vehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leading vehicle 200 is changing lanes based on the analysis performed at step 582. For example, processing unit 110 may make the determination based on a weighted average of the individual analyses performed at step 582. Under such a scheme, for example, a decision by processing unit 110 that the leading vehicle is likely changing lanes based on a particular type of analysis may be assigned a value of “1” (and “0” to represent a determination that the leading vehicle is not likely changing lanes). Different analyses performed at step 582 may be assigned different weights, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing one or more navigational responses based on stereo image analysis, consistent with disclosed embodiments. At step 610, processing unit 110 may receive a first and second plurality of images via data interface 128. For example, cameras included in image acquisition unit 120 (such as image capture devices 122 and 124 having fields of view 202 and 204) may capture a first and second plurality of images of an area forward of vehicle 200 and transmit them over a digital connection (e.g., USB, wireless, Bluetooth, etc.) to processing unit 110. In some embodiments, processing unit 110 may receive the first and second plurality of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysis module 404 to perform stereo image analysis of the first and second plurality of images to create a 3D map of the road in front of the vehicle and detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and the like. Stereo image analysis may be performed in a manner similar to the steps described in connection with FIGS. 5A-5D, above. For example, processing unit 110 may execute stereo image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road marks, traffic lights, road hazards, etc.) within the first and second plurality of images, filter out a subset of the candidate objects based on various criteria, and perform multi-frame analysis, construct measurements, and determine a confidence level for the remaining candidate objects. In performing the steps above, processing unit 110 may consider information from both the first and second plurality of images, rather than information from one set of images alone. For example, processing unit 110 may analyze the differences in pixel-level data (or other data subsets from among the two streams of captured images) for a candidate object appearing in both the first and second plurality of images. As another example, processing unit 110 may estimate a position and/or velocity of a candidate object (e.g., relative to vehicle 200) by observing that the object appears in one of the plurality of images but not the other or relative to other differences that may exist relative to objects appearing if the two image streams. For example, position, velocity, and/or acceleration relative to vehicle 200 may be determined based on trajectories, positions, movement characteristics, etc. of features associated with an object appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational response module 408 to cause one or more navigational responses in vehicle 200 based on the analysis performed at step 620 and the techniques as described above in connection with FIG. 4 . Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, a change in velocity, braking, and the like. In some embodiments, processing unit 110 may use data derived from execution of velocity and acceleration module 406 to cause the one or more navigational responses. Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing one or more navigational responses based on an analysis of three sets of images, consistent with disclosed embodiments. At step 710, processing unit 110 may receive a first, second, and third plurality of images via data interface 128. For instance, cameras included in image acquisition unit 120 (such as image capture devices 122, 124, and 126 having fields of view 202, 204, and 206) may capture a first, second, and third plurality of images of an area forward and/or to the side of vehicle 200 and transmit them over a digital connection (e.g., USB, wireless, Bluetooth, etc.) to processing unit 110. In some embodiments, processing unit 110 may receive the first, second, and third plurality of images via three or more data interfaces. For example, each of image capture devices 122, 124, 126 may have an associated data interface for communicating data to processing unit 110. The disclosed embodiments are not limited to any particular data interface configurations or protocols.

At step 720, processing unit 110 may analyze the first, second, and third plurality of images to detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and the like. The analysis may be performed in a manner similar to the steps described in connection with FIGS. 5A-5D and 6 , above. For instance, processing unit 110 may perform monocular image analysis (e.g., via execution of monocular image analysis module 402 and based on the steps described in connection with FIGS. 5A-5D, above) on each of the first, second, and third plurality of images. Alternatively, processing unit 110 may perform stereo image analysis (e.g., via execution of stereo image analysis module 404 and based on the steps described in connection with FIG. 6 , above) on the first and second plurality of images, the second and third plurality of images, and/or the first and third plurality of images. The processed information corresponding to the analysis of the first, second, and/or third plurality of images may be combined. In some embodiments, processing unit 110 may perform a combination of monocular and stereo image analyses. For example, processing unit 110 may perform monocular image analysis (e.g., via execution of monocular image analysis module 402) on the first plurality of images and stereo image analysis (e.g., via execution of stereo image analysis module 404) on the second and third plurality of images. The configuration of image capture devices 122, 124, and 126—including their respective locations and fields of view 202, 204, and 206—may influence the types of analyses conducted on the first, second, and third plurality of images. The disclosed embodiments are not limited to a particular configuration of image capture devices 122, 124, and 126, or the types of analyses conducted on the first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system 100 based on the images acquired and analyzed at steps 710 and 720. Such testing may provide an indicator of the overall performance of system 100 for certain configurations of image capture devices 122, 124, and 126. For example, processing unit 110 may determine the proportion of “false hits” (e.g., cases where system 100 incorrectly determined the presence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigational responses in vehicle 200 based on information derived from two of the first, second, and third plurality of images. Selection of two of the first, second, and third plurality of images may depend on various factors, such as, for example, the number, types, and sizes of objects detected in each of the plurality of images. Processing unit 110 may also make the selection based on image quality and resolution, the effective field of view reflected in the images, the number of captured frames, the extent to which one or more objects of interest actually appear in the frames (e.g., the percentage of frames in which an object appears, the proportion of the object that appears in each such frame, etc.), and the like.

In some embodiments, processing unit 110 may select information derived from two of the first, second, and third plurality of images by determining the extent to which information derived from one image source is consistent with information derived from other image sources. For example, processing unit 110 may combine the processed information derived from each of image capture devices 122, 124, and 126 (whether by monocular analysis, stereo analysis, or any combination of the two) and determine visual indicators (e.g., lane markings, a detected vehicle and its location and/or path, a detected traffic light, etc.) that are consistent across the images captured from each of image capture devices 122, 124, and 126. Processing unit 110 may also exclude information that is inconsistent across the captured images (e.g., a vehicle changing lanes, a lane model indicating a vehicle that is too close to vehicle 200, etc.). Thus, processing unit 110 may select information derived from two of the first, second, and third plurality of images based on the determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. Processing unit 110 may cause the one or more navigational responses based on the analysis performed at step 720 and the techniques as described above in connection with FIG. 4 . Processing unit 110 may also use data derived from execution of velocity and acceleration module 406 to cause the one or more navigational responses. In some embodiments, processing unit 110 may cause the one or more navigational responses based on a relative position, relative velocity, and/or relative acceleration between vehicle 200 and an object detected within any of the first, second, and third plurality of images. Multiple navigational responses may occur simultaneously, in sequence, or any combination thereof

Sparse Road Model for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may use a sparse map for autonomous vehicle navigation. In particular, the sparse map may be for autonomous vehicle navigation along a road segment. For example, the sparse map may provide sufficient information for navigating an autonomous vehicle without storing and/or updating a large quantity of data. As discussed below in further detail, an autonomous vehicle may use the sparse map to navigate one or more roads based on one or more stored trajectories.

Sparse Map for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may generate a sparse map for autonomous vehicle navigation. For example, the sparse map may provide sufficient information for navigation without requiring excessive data storage or data transfer rates. As discussed below in further detail, a vehicle (which may be an autonomous vehicle) may use the sparse map to navigate one or more roads. For example, in some embodiments, the sparse map may include data related to a road and potentially landmarks along the road that may be sufficient for vehicle navigation, but which also exhibit small data footprints. For example, the sparse data maps described in detail below may require significantly less storage space and data transfer bandwidth as compared with digital maps including detailed map information, such as image data collected along a road.

For example, rather than storing detailed representations of a road segment, the sparse data map may store three-dimensional polynomial representations of preferred vehicle paths along a road. These paths may require very little data storage space. Further, in the described sparse data maps, landmarks may be identified and included in the sparse map road model to aid in navigation. These landmarks may be located at any spacing suitable for enabling vehicle navigation, but in some cases, such landmarks need not be identified and included in the model at high densities and short spacings. Rather, in some cases, navigation may be possible based on landmarks that are spaced apart by at least 50 meters, at least 100 meters, at least 500 meters, at least 1 kilometer, or at least 2 kilometers. As will be discussed in more detail in other sections, the sparse map may be generated based on data collected or measured by vehicles equipped with various sensors and devices, such as image capture devices, Global Positioning System sensors, motion sensors, etc., as the vehicles travel along roadways. In some cases, the sparse map may be generated based on data collected during multiple drives of one or more vehicles along a particular roadway. Generating a sparse map using multiple drives of one or more vehicles may be referred to as “crowdsourcing” a sparse map.

Consistent with disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. For example, the disclosed systems and methods may distribute a sparse map for generating a road navigation model for an autonomous vehicle and may navigate an autonomous vehicle along a road segment using a sparse map and/or a generated road navigation model. Sparse maps consistent with the present disclosure may include one or more three-dimensional contours that may represent predetermined trajectories that autonomous vehicles may traverse as they move along associated road segments.

Sparse maps consistent with the present disclosure may also include data representing one or more road features. Such road features may include recognized landmarks, road signature profiles, and any other road-related features useful in navigating a vehicle. Sparse maps consistent with the present disclosure may enable autonomous navigation of a vehicle based on relatively small amounts of data included in the sparse map. For example, rather than including detailed representations of a road, such as road edges, road curvature, images associated with road segments, or data detailing other physical features associated with a road segment, the disclosed embodiments of the sparse map may require relatively little storage space (and relatively little bandwidth when portions of the sparse map are transferred to a vehicle) but may still adequately provide for autonomous vehicle navigation. The small data footprint of the disclosed sparse maps, discussed in further detail below, may be achieved in some embodiments by storing representations of road-related elements that require small amounts of data but still enable autonomous navigation.

For example, rather than storing detailed representations of various aspects of a road, the disclosed sparse maps may store polynomial representations of one or more trajectories that a vehicle may follow along the road. Thus, rather than storing (or having to transfer) details regarding the physical nature of the road to enable navigation along the road, using the disclosed sparse maps, a vehicle may be navigated along a particular road segment without, in some cases, having to interpret physical aspects of the road, but rather, by aligning its path of travel with a trajectory (e.g., a polynomial spline) along the particular road segment. In this way, the vehicle may be navigated based mainly upon the stored trajectory (e.g., a polynomial spline) that may require much less storage space than an approach involving storage of roadway images, road parameters, road layout, etc.

In addition to the stored polynomial representations of trajectories along a road segment, the disclosed sparse maps may also include small data objects that may represent a road feature. In some embodiments, the small data objects may include digital signatures, which are derived from a digital image (or a digital signal) that was obtained by a sensor (e.g., a camera or other sensor, such as a suspension sensor) onboard a vehicle traveling along the road segment. The digital signature may have a reduced size relative to the signal that was acquired by the sensor. In some embodiments, the digital signature may be created to be compatible with a classifier function that is configured to detect and to identify the road feature from the signal that is acquired by the sensor, for example, during a subsequent drive. In some embodiments, a digital signature may be created such that the digital signature has a footprint that is as small as possible, while retaining the ability to correlate or match the road feature with the stored signature based on an image (or a digital signal generated by a sensor, if the stored signature is not based on an image and/or includes other data) of the road feature that is captured by a camera onboard a vehicle traveling along the same road segment at a subsequent time.

In some embodiments, a size of the data objects may be further associated with a uniqueness of the road feature. For example, for a road feature that is detectable by a camera onboard a vehicle, and where the camera system onboard the vehicle is coupled to a classifier that is capable of distinguishing the image data corresponding to that road feature as being associated with a particular type of road feature, for example, a road sign, and where such a road sign is locally unique in that area (e.g., there is no identical road sign or road sign of the same type nearby), it may be sufficient to store data indicating the type of the road feature and its location.

As will be discussed in further detail below, road features (e.g., landmarks along a road segment) may be stored as small data objects that may represent a road feature in relatively few bytes, while at the same time providing sufficient information for recognizing and using such a feature for navigation. In one example, a road sign may be identified as a recognized landmark on which navigation of a vehicle may be based. A representation of the road sign may be stored in the sparse map to include, e.g., a few bytes of data indicating a type of landmark (e.g., a stop sign) and a few bytes of data indicating a location of the landmark (e.g., coordinates). Navigating based on such data-light representations of the landmarks (e.g., using representations sufficient for locating, recognizing, and navigating based upon the landmarks) may provide a desired level of navigational functionality associated with sparse maps without significantly increasing the data overhead associated with the sparse maps. This lean representation of landmarks (and other road features) may take advantage of the sensors and processors included onboard such vehicles that are configured to detect, identify, and/or classify certain road features.

When, for example, a sign or even a particular type of a sign is locally unique (e.g., when there is no other sign or no other sign of the same type) in a given area, the sparse map may use data indicating a type of a landmark (a sign or a specific type of sign), and during navigation (e.g., autonomous navigation) when a camera onboard an autonomous vehicle captures an image of the area including a sign (or of a specific type of sign), the processor may process the image, detect the sign (if indeed present in the image), classify the image as a sign (or as a specific type of sign), and correlate the location of the image with the location of the sign as stored in the sparse map.

The sparse map may include any suitable representation of objects identified along a road segment. In some cases, the objects may be referred to as semantic objects or non-semantic objects. Semantic objects may include, for example, objects associated with a predetermined type classification. This type classification may be useful in reducing the amount of data required to describe the semantic object recognized in an environment, which can be beneficial both in the harvesting phase (e.g., to reduce costs associated with bandwidth use for transferring drive information from a plurality of harvesting vehicles to a server) and during the navigation phase (e.g., reduction of map data can speed transfer of map tiles from a server to a navigating vehicle and can also reduce costs associated with bandwidth use for such transfers). Semantic object classification types may be assigned to any type of objects or features that are expected to be encountered along a roadway.

Semantic objects may further be divided into two or more logical groups. For example, in some cases, one group of semantic object types may be associated with predetermined dimensions. Such semantic objects may include certain speed limit signs, yield signs, merge signs, stop signs, traffic lights, directional arrows on a roadway, manhole covers, or any other type of object that may be associated with a standardized size. One benefit offered by such semantic objects is that very little data may be needed to represent/fully define the objects. For example, if a standardized size of a speed limit size is known, then a harvesting vehicle may need only identify (through analysis of a captured image) the presence of a speed limit sign (a recognized type) along with an indication of a position of the detected speed limit sign (e.g., a 2D position in the captured image (or, alternatively, a 3D position in real world coordinates) of a center of the sign or a certain corner of the sign) to provide sufficient information for map generation on the server side. Where 2D image positions are transmitted to the server, a position associated with the captured image where the sign was detected may also be transmitted so the server can determine a real-world position of the sign (e.g., through structure in motion techniques using multiple captured images from one or more harvesting vehicles). Even with this limited information (requiring just a few bytes to define each detected object), the server may construct the map including a fully represented speed limit sign based on the type classification (representative of a speed limit sign) received from one or more harvesting vehicles along with the position information for the detected sign.

Semantic objects may also include other recognized object or feature types that are not associated with certain standardized characteristics. Such objects or features may include potholes, tar seams, light poles, non-standardized signs, curbs, trees, tree branches, or any other type of recognized object type with one or more variable characteristics (e.g., variable dimensions). In such cases, in addition to transmitting to a server an indication of the detected object or feature type (e.g., pothole, pole, etc.) and position information for the detected object or feature, a harvesting vehicle may also transmit an indication of a size of the object or feature. The size may be expressed in 2D image dimensions (e.g., with a bounding box or one or more dimension values) or real-world dimensions (determined through structure in motion calculations, based on LIDAR or RADAR system outputs, based on trained neural network outputs, etc.).

Non-semantic objects or features may include any detectable objects or features that fall outside of a recognized category or type, but that still may provide valuable information in map generation. In some cases, such non-semantic features may include a detected corner of a building or a corner of a detected window of a building, a unique stone or object near a roadway, a concrete splatter in a roadway shoulder, or any other detectable object or feature. Upon detecting such an object or feature one or more harvesting vehicles may transmit to a map generation server a location of one or more points (2D image points or 3D real world points) associated with the detected object/feature. Additionally, a compressed or simplified image segment (e.g., an image hash) may be generated for a region of the captured image including the detected object or feature. This image hash may be calculated based on a predetermined image processing algorithm and may form an effective signature for the detected non-semantic object or feature. Such a signature may be useful for navigation relative to a sparse map including the non-semantic feature or object, as a vehicle traversing the roadway may apply an algorithm similar to the algorithm used to generate the image hash in order to confirm/verify the presence in a captured image of the mapped non-semantic feature or object. Using this technique, non-semantic features may add to the richness of the sparse maps (e.g., to enhance their usefulness in navigation) without adding significant data overhead.

As noted, target trajectories may be stored in the sparse map. These target trajectories (e.g., 3D splines) may represent the preferred or recommended paths for each available lane of a roadway, each valid pathway through a junction, for merges and exits, etc. In addition to target trajectories, other road feature may also be detected, harvested, and incorporated in the sparse maps in the form of representative splines. Such features may include, for example, road edges, lane markings, curbs, guardrails, or any other objects or features that extend along a roadway or road segment.

Generating a Sparse Map

In some embodiments, a sparse map may include at least one line representation of a road surface feature extending along a road segment and a plurality of landmarks associated with the road segment. In certain aspects, the sparse map may be generated via “crowdsourcing,” for example, through image analysis of a plurality of images acquired as one or more vehicles traverse the road segment.

FIG. 8 shows a sparse map 800 that one or more vehicles, e.g., vehicle 200 (which may be an autonomous vehicle), may access for providing autonomous vehicle navigation. Sparse map 800 may be stored in a memory, such as memory 140 or 150. Such memory devices may include any types of non-transitory storage devices or computer-readable media. For example, in some embodiments, memory 140 or 150 may include hard drives, compact discs, flash memory, magnetic based memory devices, optical based memory devices, etc. In some embodiments, sparse map 800 may be stored in a database (e.g., map database 160) that may be stored in memory 140 or 150, or other types of storage devices.

In some embodiments, sparse map 800 may be stored on a storage device or a non-transitory computer-readable medium provided onboard vehicle 200 (e.g., a storage device included in a navigation system onboard vehicle 200). A processor (e.g., processing unit 110) provided on vehicle 200 may access sparse map 800 stored in the storage device or computer-readable medium provided onboard vehicle 200 in order to generate navigational instructions for guiding the autonomous vehicle 200 as the vehicle traverses a road segment.

Sparse map 800 need not be stored locally with respect to a vehicle, however. In some embodiments, sparse map 800 may be stored on a storage device or computer-readable medium provided on a remote server that communicates with vehicle 200 or a device associated with vehicle 200. A processor (e.g., processing unit 110) provided on vehicle 200 may receive data included in sparse map 800 from the remote server and may execute the data for guiding the autonomous driving of vehicle 200. In such embodiments, the remote server may store all of sparse map 800 or only a portion thereof. Accordingly, the storage device or computer-readable medium provided onboard vehicle 200 and/or onboard one or more additional vehicles may store the remaining portion(s) of sparse map 800.

Furthermore, in such embodiments, sparse map 800 may be made accessible to a plurality of vehicles traversing various road segments (e.g., tens, hundreds, thousands, or millions of vehicles, etc.). It should be noted also that sparse map 800 may include multiple sub-maps. For example, in some embodiments, sparse map 800 may include hundreds, thousands, millions, or more, of sub-maps (e.g., map tiles) that may be used in navigating a vehicle. Such sub-maps may be referred to as local maps or map tiles, and a vehicle traveling along a roadway may access any number of local maps relevant to a location in which the vehicle is traveling. The local map sections of sparse map 800 may be stored with a Global Navigation Satellite System (GNSS) key as an index to the database of sparse map 800. Thus, while computation of steering angles for navigating a host vehicle in the present system may be performed without reliance upon a GNSS position of the host vehicle, road features, or landmarks, such GNSS information may be used for retrieval of relevant local maps.

In general, sparse map 800 may be generated based on data (e.g., drive information) collected from one or more vehicles as they travel along roadways. For example, using sensors aboard the one or more vehicles (e.g., cameras, speedometers, GPS, accelerometers, etc.), the trajectories that the one or more vehicles travel along a roadway may be recorded, and the polynomial representation of a preferred trajectory for vehicles making subsequent trips along the roadway may be determined based on the collected trajectories travelled by the one or more vehicles. Similarly, data collected by the one or more vehicles may aid in identifying potential landmarks along a particular roadway. Data collected from traversing vehicles may also be used to identify road profile information, such as road width profiles, road roughness profiles, traffic line spacing profiles, road conditions, etc. Using the collected information, sparse map 800 may be generated and distributed (e.g., for local storage or via on-the-fly data transmission) for use in navigating one or more autonomous vehicles. However, in some embodiments, map generation may not end upon initial generation of the map. As will be discussed in greater detail below, sparse map 800 may be continuously or periodically updated based on data collected from vehicles as those vehicles continue to traverse roadways included in sparse map 800.

Data recorded in sparse map 800 may include position information based on Global Positioning System (GPS) data. For example, location information may be included in sparse map 800 for various map elements, including, for example, landmark locations, road profile locations, etc. Locations for map elements included in sparse map 800 may be obtained using GPS data collected from vehicles traversing a roadway. For example, a vehicle passing an identified landmark may determine a location of the identified landmark using GPS position information associated with the vehicle and a determination of a location of the identified landmark relative to the vehicle (e.g., based on image analysis of data collected from one or more cameras on board the vehicle). Such location determinations of an identified landmark (or any other feature included in sparse map 800) may be repeated as additional vehicles pass the location of the identified landmark. Some or all of the additional location determinations may be used to refine the location information stored in sparse map 800 relative to the identified landmark. For example, in some embodiments, multiple position measurements relative to a particular feature stored in sparse map 800 may be averaged together. Any other mathematical operations, however, may also be used to refine a stored location of a map element based on a plurality of determined locations for the map element.

In a particular example, harvesting vehicles may traverse a particular road segment. Each harvesting vehicle captures images of their respective environments. The images may be collected at any suitable frame capture rate (e.g., 9 Hz, etc.). Image analysis processor(s) aboard each harvesting vehicle analyze the captured images to detect the presence of semantic and/or non-semantic features/objects. At a high level, the harvesting vehicles transmit to a mapping-server indications of detections of the semantic and/or non-semantic objects/features along with positions associated with those objects/features. In more detail, type indicators, dimension indicators, etc. may be transmitted together with the position information. The position information may include any suitable information for enabling the mapping server to aggregate the detected objects/features into a sparse map useful in navigation. In some cases, the position information may include one or more 2D image positions (e.g., X-Y pixel locations) in a captured image where the semantic or non-semantic features/objects were detected. Such image positions may correspond to a center of the feature/object, a corner, etc. In this scenario, to aid the mapping server in reconstructing the drive information and aligning the drive information from multiple harvesting vehicles, each harvesting vehicle may also provide the server with a location (e.g., a GPS location) where each image was captured.

In other cases, the harvesting vehicle may provide to the server one or more 3D real world points associated with the detected objects/features. Such 3D points may be relative to a predetermined origin (such as an origin of a drive segment) and may be determined through any suitable technique. In some cases, a structure in motion technique may be used to determine the 3D real world position of a detected object/feature. For example, a certain object such as a particular speed limit sign may be detected in two or more captured images. Using information such as the known ego motion (speed, trajectory, GPS position, etc.) of the harvesting vehicle between the captured images, along with observed changes of the speed limit sign in the captured images (change in X-Y pixel location, change in size, etc.), the real-world position of one or more points associated with the speed limit sign may be determined and passed along to the mapping server. Such an approach is optional, as it requires more computation on the part of the harvesting vehicle systems. The sparse map of the disclosed embodiments may enable autonomous navigation of a vehicle using relatively small amounts of stored data. In some embodiments, sparse map 800 may have a data density (e.g., including data representing the target trajectories, landmarks, and any other stored road features) of less than 2 MB per kilometer of roads, less than 1 MB per kilometer of roads, less than 500 kB per kilometer of roads, or less than 100 kB per kilometer of roads. In some embodiments, the data density of sparse map 800 may be less than 10 kB per kilometer of roads or even less than 2 kB per kilometer of roads (e.g., 1.6 kB per kilometer), or no more than 10 kB per kilometer of roads, or no more than 20 kB per kilometer of roads. In some embodiments, most, if not all, of the roadways of the United States may be navigated autonomously using a sparse map having a total of 4 GB or less of data. These data density values may represent an average over an entire sparse map 800, over a local map within sparse map 800, and/or over a particular road segment within sparse map 800.

As noted, sparse map 800 may include representations of a plurality of target trajectories 810 for guiding autonomous driving or navigation along a road segment. Such target trajectories may be stored as three-dimensional splines. The target trajectories stored in sparse map 800 may be determined based on two or more reconstructed trajectories of prior traversals of vehicles along a particular road segment, for example. A road segment may be associated with a single target trajectory or multiple target trajectories. For example, on a two lane road, a first target trajectory may be stored to represent an intended path of travel along the road in a first direction, and a second target trajectory may be stored to represent an intended path of travel along the road in another direction (e.g., opposite to the first direction). Additional target trajectories may be stored with respect to a particular road segment. For example, on a multi-lane road one or more target trajectories may be stored representing intended paths of travel for vehicles in one or more lanes associated with the multi-lane road. In some embodiments, each lane of a multi-lane road may be associated with its own target trajectory. In other embodiments, there may be fewer target trajectories stored than lanes present on a multi-lane road. In such cases, a vehicle navigating the multi-lane road may use any of the stored target trajectories to guides its navigation by taking into account an amount of lane offset from a lane for which a target trajectory is stored (e.g., if a vehicle is traveling in the left most lane of a three lane highway, and a target trajectory is stored only for the middle lane of the highway, the vehicle may navigate using the target trajectory of the middle lane by accounting for the amount of lane offset between the middle lane and the left-most lane when generating navigational instructions).

In some embodiments, the target trajectory may represent an ideal path that a vehicle should take as the vehicle travels. The target trajectory may be located, for example, at an approximate center of a lane of travel. In other cases, the target trajectory may be located elsewhere relative to a road segment. For example, a target trajectory may approximately coincide with a center of a road, an edge of a road, or an edge of a lane, etc. In such cases, navigation based on the target trajectory may include a determined amount of offset to be maintained relative to the location of the target trajectory. Moreover, in some embodiments, the determined amount of offset to be maintained relative to the location of the target trajectory may differ based on a type of vehicle (e.g., a passenger vehicle including two axles may have a different offset from a truck including more than two axles along at least a portion of the target trajectory).

Sparse map 800 may also include data relating to a plurality of predetermined landmarks 820 associated with particular road segments, local maps, etc. As discussed in greater detail below, these landmarks may be used in navigation of the autonomous vehicle. For example, in some embodiments, the landmarks may be used to determine a current position of the vehicle relative to a stored target trajectory. With this position information, the autonomous vehicle may be able to adjust a heading direction to match a direction of the target trajectory at the determined location.

The plurality of landmarks 820 may be identified and stored in sparse map 800 at any suitable spacing. In some embodiments, landmarks may be stored at relatively high densities (e.g., every few meters or more). In some embodiments, however, significantly larger landmark spacing values may be employed. For example, in sparse map 800, identified (or recognized) landmarks may be spaced apart by 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers. In some cases, the identified landmarks may be located at distances of even more than 2 kilometers apart.

Between landmarks, and therefore between determinations of vehicle position relative to a target trajectory, the vehicle may navigate based on dead reckoning in which the vehicle uses sensors to determine its ego motion and estimate its position relative to the target trajectory. Because errors may accumulate during navigation by dead reckoning, over time the position determinations relative to the target trajectory may become increasingly less accurate. The vehicle may use landmarks occurring in sparse map 800 (and their known locations) to remove the dead reckoning-induced errors in position determination. In this way, the identified landmarks included in sparse map 800 may serve as navigational anchors from which an accurate position of the vehicle relative to a target trajectory may be determined. Because a certain amount of error may be acceptable in position location, an identified landmark need not always be available to an autonomous vehicle. Rather, suitable navigation may be possible even based on landmark spacings, as noted above, of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or more. In some embodiments, a density of 1 identified landmark every 1 km of road may be sufficient to maintain a longitudinal position determination accuracy within 1 m. Thus, not every potential landmark appearing along a road segment need be stored in sparse map 800.

Moreover, in some embodiments, lane markings may be used for localization of the vehicle during landmark spacings. By using lane markings during landmark spacings, the accumulation of errors during navigation by dead reckoning may be minimized.

In addition to target trajectories and identified landmarks, sparse map 800 may include information relating to various other road features. For example, FIG. 9A illustrates a representation of curves along a particular road segment that may be stored in sparse map 800. In some embodiments, a single lane of a road may be modeled by a three-dimensional polynomial description of left and right sides of the road. Such polynomials representing left and right sides of a single lane are shown in FIG. 9A. Regardless of how many lanes a road may have, the road may be represented using polynomials in a way similar to that illustrated in FIG. 9A. For example, left and right sides of a multi-lane road may be represented by polynomials similar to those shown in FIG. 9A, and intermediate lane markings included on a multi-lane road (e.g., dashed markings representing lane boundaries, solid yellow lines representing boundaries between lanes traveling in different directions, etc.) may also be represented using polynomials such as those shown in FIG. 9A.

As shown in FIG. 9A, a lane 900 may be represented using polynomials (e.g., a first order, second order, third order, or any suitable order polynomials). For illustration, lane 900 is shown as a two-dimensional lane and the polynomials are shown as two-dimensional polynomials. As depicted in FIG. 9A, lane 900 includes a left side 910 and a right side 920. In some embodiments, more than one polynomial may be used to represent a location of each side of the road or lane boundary. For example, each of left side 910 and right side 920 may be represented by a plurality of polynomials of any suitable length. In some cases, the polynomials may have a length of about 100 m, although other lengths greater than or less than 100 m may also be used. Additionally, the polynomials can overlap with one another in order to facilitate seamless transitions in navigating based on subsequently encountered polynomials as a host vehicle travels along a roadway. For example, each of left side 910 and right side 920 may be represented by a plurality of third order polynomials separated into segments of about 100 meters in length (an example of the first predetermined range), and overlapping each other by about 50 meters. The polynomials representing the left side 910 and the right side 920 may or may not have the same order. For example, in some embodiments, some polynomials may be second order polynomials, some may be third order polynomials, and some may be fourth order polynomials.

In the example shown in FIG. 9A, left side 910 of lane 900 is represented by two groups of third order polynomials. The first group includes polynomial segments 911, 912, and 913. The second group includes polynomial segments 914, 915, and 916. The two groups, while substantially parallel to each other, follow the locations of their respective sides of the road. Polynomial segments 911, 912, 913, 914, 915, and 916 have a length of about 100 meters and overlap adjacent segments in the series by about 50 meters. As noted previously, however, polynomials of different lengths and different overlap amounts may also be used. For example, the polynomials may have lengths of 500 m, 1 km, or more, and the overlap amount may vary from 0 to 50 m, 50 m to 100 m, or greater than 100 m. Additionally, while FIG. 9A is shown as representing polynomials extending in 2D space (e.g., on the surface of the paper), it is to be understood that these polynomials may represent curves extending in three dimensions (e.g., including a height component) to represent elevation changes in a road segment in addition to X-Y curvature. In the example shown in FIG. 9A, right side 920 of lane 900 is further represented by a first group having polynomial segments 921, 922, and 923 and a second group having polynomial segments 924, 925, and 926.

Returning to the target trajectories of sparse map 800, FIG. 9B shows a three-dimensional polynomial representing a target trajectory for a vehicle traveling along a particular road segment. The target trajectory represents not only the X-Y path that a host vehicle should travel along a particular road segment, but also the elevation change that the host vehicle will experience when traveling along the road segment. Thus, each target trajectory in sparse map 800 may be represented by one or more three-dimensional polynomials, like the three-dimensional polynomial 950 shown in FIG. 9B. Sparse map 800 may include a plurality of trajectories (e.g., millions or billions or more to represent trajectories of vehicles along various road segments along roadways throughout the world). In some embodiments, each target trajectory may correspond to a spline connecting three-dimensional polynomial segments.

Regarding the data footprint of polynomial curves stored in sparse map 800, in some embodiments, each third degree polynomial may be represented by four parameters, each requiring four bytes of data. Suitable representations may be obtained with third degree polynomials requiring about 192 bytes of data for every 100 m. This may translate to approximately 200 kB per hour in data usage/transfer requirements for a host vehicle traveling approximately 100 km/hr.

Sparse map 800 may describe the lanes network using a combination of geometry descriptors and meta-data. The geometry may be described by polynomials or splines as described above. The meta-data may describe the number of lanes, special characteristics (such as a car pool lane), and possibly other sparse labels. The total footprint of such indicators may be negligible.

Accordingly, a sparse map according to embodiments of the present disclosure may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature. In some embodiments, as discussed above, the at least one line representation of the road surface feature may include a spline, a polynomial representation, or a curve. Furthermore, in some embodiments, the road surface feature may include at least one of a road edge or a lane marking. Moreover, as discussed below with respect to “crowdsourcing,” the road surface feature may be identified through image analysis of a plurality of images acquired as one or more vehicles traverse the road segment.

As previously noted, sparse map 800 may include a plurality of predetermined landmarks associated with a road segment. Rather than storing actual images of the landmarks and relying, for example, on image recognition analysis based on captured images and stored images, each landmark in sparse map 800 may be represented and recognized using less data than a stored, actual image would require. Data representing landmarks may still include sufficient information for describing or identifying the landmarks along a road. Storing data describing characteristics of landmarks, rather than the actual images of landmarks, may reduce the size of sparse map 800.

FIG. 10 illustrates examples of types of landmarks that may be represented in sparse map 800. The landmarks may include any visible and identifiable objects along a road segment. The landmarks may be selected such that they are fixed and do not change often with respect to their locations and/or content. The landmarks included in sparse map 800 may be useful in determining a location of vehicle 200 with respect to a target trajectory as the vehicle traverses a particular road segment. Examples of landmarks may include traffic signs, directional signs, general signs (e.g., rectangular signs), roadside fixtures (e.g., lampposts, reflectors, etc.), and any other suitable category. In some embodiments, lane marks on the road, may also be included as landmarks in sparse map 800.

Examples of landmarks shown in FIG. 10 include traffic signs, directional signs, roadside fixtures, and general signs. Traffic signs may include, for example, speed limit signs (e.g., speed limit sign 1000), yield signs (e.g., yield sign 1005), route number signs (e.g., route number sign 1010), traffic light signs (e.g., traffic light sign 1015), stop signs (e.g., stop sign 1020). Directional signs may include a sign that includes one or more arrows indicating one or more directions to different places. For example, directional signs may include a highway sign 1025 having arrows for directing vehicles to different roads or places, an exit sign 1030 having an arrow directing vehicles off a road, etc. Accordingly, at least one of the plurality of landmarks may include a road sign.

General signs may be unrelated to traffic. For example, general signs may include billboards used for advertisement, or a welcome board adjacent a border between two countries, states, counties, cities, or towns. FIG. 10 shows a general sign 1040 (“Joe's Restaurant”). Although general sign 1040 may have a rectangular shape, as shown in FIG. 10 , general sign 1040 may have other shapes, such as square, circle, triangle, etc.

Landmarks may also include roadside fixtures. Roadside fixtures may be objects that are not signs, and may not be related to traffic or directions. For example, roadside fixtures may include lampposts (e.g., lamppost 1035), power line posts, traffic light posts, etc.

Landmarks may also include beacons that may be specifically designed for usage in an autonomous vehicle navigation system. For example, such beacons may include stand-alone structures placed at predetermined intervals to aid in navigating a host vehicle. Such beacons may also include visual/graphical information added to existing road signs (e.g., icons, emblems, bar codes, etc.) that may be identified or recognized by a vehicle traveling along a road segment. Such beacons may also include electronic components. In such embodiments, electronic beacons (e.g., RFID tags, etc.) may be used to transmit non-visual information to a host vehicle. Such information may include, for example, landmark identification and/or landmark location information that a host vehicle may use in determining its position along a target trajectory.

In some embodiments, the landmarks included in sparse map 800 may be represented by a data object of a predetermined size. The data representing a landmark may include any suitable parameters for identifying a particular landmark. For example, in some embodiments, landmarks stored in sparse map 800 may include parameters such as a physical size of the landmark (e.g., to support estimation of distance to the landmark based on a known size/scale), a distance to a previous landmark, lateral offset, height, a type code (e.g., a landmark type—what type of directional sign, traffic sign, etc.), a GPS coordinate (e.g., to support global localization), and any other suitable parameters. Each parameter may be associated with a data size. For example, a landmark size may be stored using 8 bytes of data. A distance to a previous landmark, a lateral offset, and height may be specified using 12 bytes of data. A type code associated with a landmark such as a directional sign or a traffic sign may require about 2 bytes of data. For general signs, an image signature enabling identification of the general sign may be stored using 50 bytes of data storage. The landmark GPS position may be associated with 16 bytes of data storage. These data sizes for each parameter are examples only, and other data sizes may also be used. Representing landmarks in sparse map 800 in this manner may offer a lean solution for efficiently representing landmarks in the database. In some embodiments, objects may be referred to as standard semantic objects or non-standard semantic objects. A standard semantic object may include any class of object for which there's a standardized set of characteristics (e.g., speed limit signs, warning signs, directional signs, traffic lights, etc. having known dimensions or other characteristics). A non-standard semantic object may include any object that is not associated with a standardized set of characteristics (e.g., general advertising signs, signs identifying business establishments, potholes, trees, etc. that may have variable dimensions). Each non-standard semantic object may be represented with 38 bytes of data (e.g., 8 bytes for size; 12 bytes for distance to previous landmark, lateral offset, and height; 2 bytes for a type code; and 16 bytes for position coordinates). Standard semantic objects may be represented using even less data, as size information may not be needed by the mapping server to fully represent the object in the sparse map.

Sparse map 800 may use a tag system to represent landmark types. In some cases, each traffic sign or directional sign may be associated with its own tag, which may be stored in the database as part of the landmark identification. For example, the database may include on the order of 1000 different tags to represent various traffic signs and on the order of about 10000 different tags to represent directional signs. Of course, any suitable number of tags may be used, and additional tags may be created as needed. General purpose signs may be represented in some embodiments using less than about 100 bytes (e.g., about 86 bytes including 8 bytes for size; 12 bytes for distance to previous landmark, lateral offset, and height; 50 bytes for an image signature; and 16 bytes for GPS coordinates).

Thus, for semantic road signs not requiring an image signature, the data density impact to sparse map 800, even at relatively high landmark densities of about 1 per 50 m, may be on the order of about 760 bytes per kilometer (e.g., 20 landmarks per km×38 bytes per landmark=760 bytes). Even for general purpose signs including an image signature component, the data density impact is about 1.72 kB per km (e.g., 20 landmarks per km×86 bytes per landmark=1,720 bytes). For semantic road signs, this equates to about 76 kB per hour of data usage for a vehicle traveling 100 km/hr. For general purpose signs, this equates to about 170 kB per hour for a vehicle traveling 100 km/hr. It should be noted that in some environments (e.g., urban environments) there may be a much higher density of detected objects available for inclusion in the sparse map (perhaps more than one per meter). In some embodiments, a generally rectangular object, such as a rectangular sign, may be represented in sparse map 800 by no more than 100 bytes of data. The representation of the generally rectangular object (e.g., general sign 1040) in sparse map 800 may include a condensed image signature or image hash (e.g., condensed image signature 1045) associated with the generally rectangular object. This condensed image signature/image hash may be determined using any suitable image hashing algorithm and may be used, for example, to aid in identification of a general purpose sign, for example, as a recognized landmark. Such a condensed image signature (e.g., image information derived from actual image data representing an object) may avoid a need for storage of an actual image of an object or a need for comparative image analysis performed on actual images in order to recognize landmarks.

Referring to FIG. 10 , sparse map 800 may include or store a condensed image signature 1045 associated with a general sign 1040, rather than an actual image of general sign 1040. For example, after an image capture device (e.g., image capture device 122, 124, or 126) captures an image of general sign 1040, a processor (e.g., image processor 190 or any other processor that can process images either aboard or remotely located relative to a host vehicle) may perform an image analysis to extract/create condensed image signature 1045 that includes a unique signature or pattern associated with general sign 1040. In one embodiment, condensed image signature 1045 may include a shape, color pattern, a brightness pattern, or any other feature that may be extracted from the image of general sign 1040 for describing general sign 1040.

For example, in FIG. 10 , the circles, triangles, and stars shown in condensed image signature 1045 may represent areas of different colors. The pattern represented by the circles, triangles, and stars may be stored in sparse map 800, e.g., within the 50 bytes designated to include an image signature. Notably, the circles, triangles, and stars are not necessarily meant to indicate that such shapes are stored as part of the image signature. Rather, these shapes are meant to conceptually represent recognizable areas having discernible color differences, textual areas, graphical shapes, or other variations in characteristics that may be associated with a general purpose sign. Such condensed image signatures can be used to identify a landmark in the form of a general sign. For example, the condensed image signature can be used to perform a same-not-same analysis based on a comparison of a stored condensed image signature with image data captured, for example, using a camera onboard an autonomous vehicle.

Accordingly, the plurality of landmarks may be identified through image analysis of the plurality of images acquired as one or more vehicles traverse the road segment. As explained below with respect to “crowdsourcing,” in some embodiments, the image analysis to identify the plurality of landmarks may include accepting potential landmarks when a ratio of images in which the landmark does appear to images in which the landmark does not appear exceeds a threshold. Furthermore, in some embodiments, the image analysis to identify the plurality of landmarks may include rejecting potential landmarks when a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.

Returning to the target trajectories a host vehicle may use to navigate a particular road segment, FIG. 11A shows polynomial representations trajectories capturing during a process of building or maintaining sparse map 800. A polynomial representation of a target trajectory included in sparse map 800 may be determined based on two or more reconstructed trajectories of prior traversals of vehicles along the same road segment. In some embodiments, the polynomial representation of the target trajectory included in sparse map 800 may be an aggregation of two or more reconstructed trajectories of prior traversals of vehicles along the same road segment. In some embodiments, the polynomial representation of the target trajectory included in sparse map 800 may be an average of the two or more reconstructed trajectories of prior traversals of vehicles along the same road segment. Other mathematical operations may also be used to construct a target trajectory along a road path based on reconstructed trajectories collected from vehicles traversing along a road segment.

As shown in FIG. 11A, a road segment 1100 may be travelled by a number of vehicles 200 at different times. Each vehicle 200 may collect data relating to a path that the vehicle took along the road segment. The path traveled by a particular vehicle may be determined based on camera data, accelerometer information, speed sensor information, and/or GPS information, among other potential sources. Such data may be used to reconstruct trajectories of vehicles traveling along the road segment, and based on these reconstructed trajectories, a target trajectory (or multiple target trajectories) may be determined for the particular road segment. Such target trajectories may represent a preferred path of a host vehicle (e.g., guided by an autonomous navigation system) as the vehicle travels along the road segment.

In the example shown in FIG. 11A, a first reconstructed trajectory 1101 may be determined based on data received from a first vehicle traversing road segment 1100 at a first time period (e.g., day 1), a second reconstructed trajectory 1102 may be obtained from a second vehicle traversing road segment 1100 at a second time period (e.g., day 2), and a third reconstructed trajectory 1103 may be obtained from a third vehicle traversing road segment 1100 at a third time period (e.g., day 3). Each trajectory 1101, 1102, and 1103 may be represented by a polynomial, such as a three-dimensional polynomial. It should be noted that in some embodiments, any of the reconstructed trajectories may be assembled onboard the vehicles traversing road segment 1100.

Additionally, or alternatively, such reconstructed trajectories may be determined on a server side based on information received from vehicles traversing road segment 1100. For example, in some embodiments, vehicles 200 may transmit data to one or more servers relating to their motion along road segment 1100 (e.g., steering angle, heading, time, position, speed, sensed road geometry, and/or sensed landmarks, among things). The server may reconstruct trajectories for vehicles 200 based on the received data. The server may also generate a target trajectory for guiding navigation of autonomous vehicle that will travel along the same road segment 1100 at a later time based on the first, second, and third trajectories 1101, 1102, and 1103. While a target trajectory may be associated with a single prior traversal of a road segment, in some embodiments, each target trajectory included in sparse map 800 may be determined based on two or more reconstructed trajectories of vehicles traversing the same road segment. In FIG. 11A, the target trajectory is represented by 1110. In some embodiments, the target trajectory 1110 may be generated based on an average of the first, second, and third trajectories 1101, 1102, and 1103. In some embodiments, the target trajectory 1110 included in sparse map 800 may be an aggregation (e.g., a weighted combination) of two or more reconstructed trajectories.

At the mapping server, the server may receive actual trajectories for a particular road segment from multiple harvesting vehicles traversing the road segment. To generate a target trajectory for each valid path along the road segment (e.g., each lane, each drive direction, each path through a junction, etc.), the received actual trajectories may be aligned. The alignment process may include using detected objects/features identified along the road segment along with harvested positions of those detected objects/features to correlate the actual, harvested trajectories with one another. Once aligned, an average or “best fit” target trajectory for each available lane, etc. may be determined based on the aggregated, correlated/aligned actual trajectories.

FIGS. 11B and 11C further illustrate the concept of target trajectories associated with road segments present within a geographic region 1111. As shown in FIG. 11B, a first road segment 1120 within geographic region 1111 may include a multilane road, which includes two lanes 1122 designated for vehicle travel in a first direction and two additional lanes 1124 designated for vehicle travel in a second direction opposite to the first direction. Lanes 1122 and lanes 1124 may be separated by a double yellow line 1123. Geographic region 1111 may also include a branching road segment 1130 that intersects with road segment 1120. Road segment 1130 may include a two-lane road, each lane being designated for a different direction of travel. Geographic region 1111 may also include other road features, such as a stop line 1132, a stop sign 1134, a speed limit sign 1136, and a hazard sign 1138.

As shown in FIG. 11C, sparse map 800 may include a local map 1140 including a road model for assisting with autonomous navigation of vehicles within geographic region 1111. For example, local map 1140 may include target trajectories for one or more lanes associated with road segments 1120 and/or 1130 within geographic region 1111. For example, local map 1140 may include target trajectories 1141 and/or 1142 that an autonomous vehicle may access or rely upon when traversing lanes 1122. Similarly, local map 1140 may include target trajectories 1143 and/or 1144 that an autonomous vehicle may access or rely upon when traversing lanes 1124. Further, local map 1140 may include target trajectories 1145 and/or 1146 that an autonomous vehicle may access or rely upon when traversing road segment 1130. Target trajectory 1147 represents a preferred path an autonomous vehicle should follow when transitioning from lanes 1120 (and specifically, relative to target trajectory 1141 associated with a right-most lane of lanes 1120) to road segment 1130 (and specifically, relative to a target trajectory 1145 associated with a first side of road segment 1130. Similarly, target trajectory 1148 represents a preferred path an autonomous vehicle should follow when transitioning from road segment 1130 (and specifically, relative to target trajectory 1146) to a portion of road segment 1124 (and specifically, as shown, relative to a target trajectory 1143 associated with a left lane of lanes 1124.

Sparse map 800 may also include representations of other road-related features associated with geographic region 1111. For example, sparse map 800 may also include representations of one or more landmarks identified in geographic region 1111. Such landmarks may include a first landmark 1150 associated with stop line 1132, a second landmark 1152 associated with stop sign 1134, a third landmark associated with speed limit sign 1154, and a fourth landmark 1156 associated with hazard sign 1138. Such landmarks may be used, for example, to assist an autonomous vehicle in determining its current location relative to any of the shown target trajectories, such that the vehicle may adjust its heading to match a direction of the target trajectory at the determined location.

In some embodiments, sparse map 800 may also include road signature profiles. Such road signature profiles may be associated with any discernible/measurable variation in at least one parameter associated with a road. For example, in some cases, such profiles may be associated with variations in road surface information such as variations in surface roughness of a particular road segment, variations in road width over a particular road segment, variations in distances between dashed lines painted along a particular road segment, variations in road curvature along a particular road segment, etc. FIG. 11D shows an example of a road signature profile 1160. While profile 1160 may represent any of the parameters mentioned above, or others, in one example, profile 1160 may represent a measure of road surface roughness, as obtained, for example, by monitoring one or more sensors providing outputs indicative of an amount of suspension displacement as a vehicle travels a particular road segment.

Alternatively or concurrently, profile 1160 may represent variation in road width, as determined based on image data obtained via a camera onboard a vehicle traveling a particular road segment. Such profiles may be useful, for example, in determining a particular location of an autonomous vehicle relative to a particular target trajectory. That is, as it traverses a road segment, an autonomous vehicle may measure a profile associated with one or more parameters associated with the road segment. If the measured profile can be correlated/matched with a predetermined profile that plots the parameter variation with respect to position along the road segment, then the measured and predetermined profiles may be used (e.g., by overlaying corresponding sections of the measured and predetermined profiles) in order to determine a current position along the road segment and, therefore, a current position relative to a target trajectory for the road segment.

In some embodiments, sparse map 800 may include different trajectories based on different characteristics associated with a user of autonomous vehicles, environmental conditions, and/or other parameters relating to driving. For example, in some embodiments, different trajectories may be generated based on different user preferences and/or profiles. Sparse map 800 including such different trajectories may be provided to different autonomous vehicles of different users. For example, some users may prefer to avoid toll roads, while others may prefer to take the shortest or fastest routes, regardless of whether there is a toll road on the route. The disclosed systems may generate different sparse maps with different trajectories based on such different user preferences or profiles. As another example, some users may prefer to travel in a fast moving lane, while others may prefer to maintain a position in the central lane at all times.

Different trajectories may be generated and included in sparse map 800 based on different environmental conditions, such as day and night, snow, rain, fog, etc. Autonomous vehicles driving under different environmental conditions may be provided with sparse map 800 generated based on such different environmental conditions. In some embodiments, cameras provided on autonomous vehicles may detect the environmental conditions, and may provide such information back to a server that generates and provides sparse maps. For example, the server may generate or update an already generated sparse map 800 to include trajectories that may be more suitable or safer for autonomous driving under the detected environmental conditions. The update of sparse map 800 based on environmental conditions may be performed dynamically as the autonomous vehicles are traveling along roads.

Other different parameters relating to driving may also be used as a basis for generating and providing different sparse maps to different autonomous vehicles. For example, when an autonomous vehicle is traveling at a high speed, turns may be tighter. Trajectories associated with specific lanes, rather than roads, may be included in sparse map 800 such that the autonomous vehicle may maintain within a specific lane as the vehicle follows a specific trajectory. When an image captured by a camera onboard the autonomous vehicle indicates that the vehicle has drifted outside of the lane (e.g., crossed the lane mark), an action may be triggered within the vehicle to bring the vehicle back to the designated lane according to the specific trajectory.

Crowdsourcing a Sparse Map

The disclosed sparse maps may be efficiently (and passively) generated through the power of crowdsourcing. For example, any private or commercial vehicle equipped with a camera (e.g., a simple, low resolution camera regularly included as OEM equipment on today's vehicles) and an appropriate image analysis processor can serve as a harvesting vehicle. No special equipment (e.g., high definition imaging and/or positioning systems) are required. As a result of the disclosed crowdsourcing technique, the generated sparse maps may be extremely accurate and may include extremely refined position information (enabling navigation error limits of 10 cm or less) without requiring any specialized imaging or sensing equipment as input to the map generation process. Crowdsourcing also enables much more rapid (and inexpensive) updates to the generated maps, as new drive information is continuously available to the mapping server system from any roads traversed by private or commercial vehicles minimally equipped to also serve as harvesting vehicles. There is no need for designated vehicles equipped with high-definition imaging and mapping sensors. Therefore, the expense associated with building such specialized vehicles can be avoided. Further, updates to the presently disclosed sparse maps may be made much more rapidly than systems that rely upon dedicated, specialized mapping vehicles (which by virtue of their expense and special equipment are typically limited to a fleet of specialized vehicles of far lower numbers than the number of private or commercial vehicles already available for performing the disclosed harvesting techniques).

The disclosed sparse maps generated through crowdsourcing may be extremely accurate because they may be generated based on many inputs from multiple (10s, hundreds, millions, etc.) of harvesting vehicles that have collected drive information along a particular road segment. For example, every harvesting vehicle that drives along a particular road segment may record its actual trajectory and may determine position information relative to detected objects/features along the road segment. This information is passed along from multiple harvesting vehicles to a server. The actual trajectories are aggregated to generate a refined, target trajectory for each valid drive path along the road segment. Additionally, the position information collected from the multiple harvesting vehicles for each of the detected objects/features along the road segment (semantic or non-semantic) can also be aggregated. As a result, the mapped position of each detected object/feature may constitute an average of hundreds, thousands, or millions of individually determined positions for each detected object/feature. Such a technique may yield extremely accurate mapped positions for the detected objects/features.

In some embodiments, the disclosed systems and methods may generate a sparse map for autonomous vehicle navigation. For example, disclosed systems and methods may use crowdsourced data for generation of a sparse map that one or more autonomous vehicles may use to navigate along a system of roads. As used herein, “crowdsourcing” means that data are received from various vehicles (e.g., autonomous vehicles) travelling on a road segment at different times, and such data are used to generate and/or update the road model, including sparse map tiles. The model or any of its sparse map tiles may, in turn, be transmitted to the vehicles or other vehicles later travelling along the road segment for assisting autonomous vehicle navigation. The road model may include a plurality of target trajectories representing preferred trajectories that autonomous vehicles should follow as they traverse a road segment. The target trajectories may be the same as a reconstructed actual trajectory collected from a vehicle traversing a road segment, which may be transmitted from the vehicle to a server. In some embodiments, the target trajectories may be different from actual trajectories that one or more vehicles previously took when traversing a road segment. The target trajectories may be generated based on actual trajectories (e.g., through averaging or any other suitable operation).

The vehicle trajectory data that a vehicle may upload to a server may correspond with the actual reconstructed trajectory for the vehicle or may correspond to a recommended trajectory, which may be based on or related to the actual reconstructed trajectory of the vehicle, but may differ from the actual reconstructed trajectory. For example, vehicles may modify their actual, reconstructed trajectories and submit (e.g., recommend) to the server the modified actual trajectories. The road model may use the recommended, modified trajectories as target trajectories for autonomous navigation of other vehicles.

In addition to trajectory information, other information for potential use in building a sparse data map 800 may include information relating to potential landmark candidates. For example, through crowd sourcing of information, the disclosed systems and methods may identify potential landmarks in an environment and refine landmark positions. The landmarks may be used by a navigation system of autonomous vehicles to determine and/or adjust the position of the vehicle along the target trajectories.

The reconstructed trajectories that a vehicle may generate as the vehicle travels along a road may be obtained by any suitable method. In some embodiments, the reconstructed trajectories may be developed by stitching together segments of motion for the vehicle, using, e.g., ego motion estimation (e.g., three dimensional translation and three dimensional rotation of the camera, and hence the body of the vehicle). The rotation and translation estimation may be determined based on analysis of images captured by one or more image capture devices along with information from other sensors or devices, such as inertial sensors and speed sensors. For example, the inertial sensors may include an accelerometer or other suitable sensors configured to measure changes in translation and/or rotation of the vehicle body. The vehicle may include a speed sensor that measures a speed of the vehicle.

In some embodiments, the ego motion of the camera (and hence the vehicle body) may be estimated based on an optical flow analysis of the captured images. An optical flow analysis of a sequence of images identifies movement of pixels from the sequence of images, and based on the identified movement, determines motions of the vehicle. The ego motion may be integrated over time and along the road segment to reconstruct a trajectory associated with the road segment that the vehicle has followed.

Data (e.g., reconstructed trajectories) collected by multiple vehicles in multiple drives along a road segment at different times may be used to construct the road model (e.g., including the target trajectories, etc.) included in sparse data map 800. Data collected by multiple vehicles in multiple drives along a road segment at different times may also be averaged to increase an accuracy of the model. In some embodiments, data regarding the road geometry and/or landmarks may be received from multiple vehicles that travel through the common road segment at different times. Such data received from different vehicles may be combined to generate the road model and/or to update the road model.

The geometry of a reconstructed trajectory (and also a target trajectory) along a road segment may be represented by a curve in three dimensional space, which may be a spline connecting three dimensional polynomials. The reconstructed trajectory curve may be determined from analysis of a video stream or a plurality of images captured by a camera installed on the vehicle. In some embodiments, a location is identified in each frame or image that is a few meters ahead of the current position of the vehicle. This location is where the vehicle is expected to travel to in a predetermined time period. This operation may be repeated frame by frame, and at the same time, the vehicle may compute the camera's ego motion (rotation and translation). At each frame or image, a short range model for the desired path is generated by the vehicle in a reference frame that is attached to the camera. The short range models may be stitched together to obtain a three dimensional model of the road in some coordinate frame, which may be an arbitrary or predetermined coordinate frame. The three dimensional model of the road may then be fitted by a spline, which may include or connect one or more polynomials of suitable orders.

To conclude the short range road model at each frame, one or more detection modules may be used. For example, a bottom-up lane detection module may be used. The bottom-up lane detection module may be useful when lane marks are drawn on the road. This module may look for edges in the image and assembles them together to form the lane marks. A second module may be used together with the bottom-up lane detection module. The second module is an end-to-end deep neural network, which may be trained to predict the correct short range path from an input image. In both modules, the road model may be detected in the image coordinate frame and transformed to a three dimensional space that may be virtually attached to the camera.

Although the reconstructed trajectory modeling method may introduce an accumulation of errors due to the integration of ego motion over a long period of time, which may include a noise component, such errors may be inconsequential as the generated model may provide sufficient accuracy for navigation over a local scale. In addition, it is possible to cancel the integrated error by using external sources of information, such as satellite images or geodetic measurements. For example, the disclosed systems and methods may use a GNSS receiver to cancel accumulated errors. However, the GNSS positioning signals may not be always available and accurate. The disclosed systems and methods may enable a steering application that depends weakly on the availability and accuracy of GNSS positioning. In such systems, the usage of the GNSS signals may be limited. For example, in some embodiments, the disclosed systems may use the GNSS signals for database indexing purposes only.

In some embodiments, the range scale (e.g., local scale) that may be relevant for an autonomous vehicle navigation steering application may be on the order of 50 meters, 100 meters, 200 meters, 300 meters, etc. Such distances may be used, as the geometrical road model is mainly used for two purposes: planning the trajectory ahead and localizing the vehicle on the road model. In some embodiments, the planning task may use the model over a typical range of 40 meters ahead (or any other suitable distance ahead, such as 20 meters, 30 meters, 50 meters), when the control algorithm steers the vehicle according to a target point located 1.3 seconds ahead (or any other time such as 1.5 seconds, 1.7 seconds, 2 seconds, etc.). The localization task uses the road model over a typical range of 60 meters behind the car (or any other suitable distances, such as 50 meters, 100 meters, 150 meters, etc.), according to a method called “tail alignment” described in more detail in another section. The disclosed systems and methods may generate a geometrical model that has sufficient accuracy over particular range, such as 100 meters, such that a planned trajectory will not deviate by more than, for example, 30 cm from the lane center.

As explained above, a three dimensional road model may be constructed from detecting short range sections and stitching them together. The stitching may be enabled by computing a six degree ego motion model, using the videos and/or images captured by the camera, data from the inertial sensors that reflect the motions of the vehicle, and the host vehicle velocity signal. The accumulated error may be small enough over some local range scale, such as of the order of 100 meters. All this may be completed in a single drive over a particular road segment.

In some embodiments, multiple drives may be used to average the resulted model, and to increase its accuracy further. The same car may travel the same route multiple times, or multiple cars may send their collected model data to a central server. In any case, a matching procedure may be performed to identify overlapping models and to enable averaging in order to generate target trajectories. The constructed model (e.g., including the target trajectories) may be used for steering once a convergence criterion is met. Subsequent drives may be used for further model improvements and in order to accommodate infrastructure changes.

Sharing of driving experience (such as sensed data) between multiple cars becomes feasible if they are connected to a central server. Each vehicle client may store a partial copy of a universal road model, which may be relevant for its current position. A bidirectional update procedure between the vehicles and the server may be performed by the vehicles and the server. The small footprint concept discussed above enables the disclosed systems and methods to perform the bidirectional updates using a very small bandwidth.

Information relating to potential landmarks may also be determined and forwarded to a central server. For example, the disclosed systems and methods may determine one or more physical properties of a potential landmark based on one or more images that include the landmark. The physical properties may include a physical size (e.g., height, width) of the landmark, a distance from a vehicle to a landmark, a distance between the landmark to a previous landmark, the lateral position of the landmark (e.g., the position of the landmark relative to the lane of travel), the GPS coordinates of the landmark, a type of landmark, identification of text on the landmark, etc. For example, a vehicle may analyze one or more images captured by a camera to detect a potential landmark, such as a speed limit sign.

The vehicle may determine a distance from the vehicle to the landmark or a position associated with the landmark (e.g., any semantic or non-semantic object or feature along a road segment) based on the analysis of the one or more images. In some embodiments, the distance may be determined based on analysis of images of the landmark using a suitable image analysis method, such as a scaling method and/or an optical flow method. As previously noted, a position of the object/feature may include a 2D image position (e.g., an X-Y pixel position in one or more captured images) of one or more points associated with the object/feature or may include a 3D real-world position of one or more points (e.g., determined through structure in motion/optical flow techniques, LIDAR or RADAR information, etc.).

In some embodiments, the disclosed systems and methods may be configured to determine a type or classification of a potential landmark. In case the vehicle determines that a certain potential landmark corresponds to a predetermined type or classification stored in a sparse map, it may be sufficient for the vehicle to communicate to the server an indication of the type or classification of the landmark, along with its location. The server may store such indications. At a later time, during navigation, a navigating vehicle may capture an image that includes a representation of the landmark, process the image (e.g., using a classifier), and compare the result landmark in order to confirm detection of the mapped landmark and to use the mapped landmark in localizing the navigating vehicle relative to the sparse map.

In some embodiments, multiple autonomous vehicles travelling on a road segment may communicate with a server. The vehicles (or clients) may generate a curve describing its drive (e.g., through ego motion integration) in an arbitrary coordinate frame. The vehicles may detect landmarks and locate them in the same frame. The vehicles may upload the curve and the landmarks to the server. The server may collect data from vehicles over multiple drives, and generate a unified road model. For example, as discussed below with respect to FIG. 19 , the server may generate a sparse map having the unified road model using the uploaded curves and landmarks.

The server may also distribute the model to clients (e.g., vehicles). For example, the server may distribute the sparse map to one or more vehicles. The server may continuously or periodically update the model when receiving new data from the vehicles. For example, the server may process the new data to evaluate whether the data includes information that should trigger an updated, or creation of new data on the server. The server may distribute the updated model or the updates to the vehicles for providing autonomous vehicle navigation.

The server may use one or more criteria for determining whether new data received from the vehicles should trigger an update to the model or trigger creation of new data. For example, when the new data indicates that a previously recognized landmark at a specific location no longer exists, or is replaced by another landmark, the server may determine that the new data should trigger an update to the model. As another example, when the new data indicates that a road segment has been closed, and when this has been corroborated by data received from other vehicles, the server may determine that the new data should trigger an update to the model.

The server may distribute the updated model (or the updated portion of the model) to one or more vehicles that are traveling on the road segment, with which the updates to the model are associated. The server may also distribute the updated model to vehicles that are about to travel on the road segment, or vehicles whose planned trip includes the road segment, with which the updates to the model are associated. For example, while an autonomous vehicle is traveling along another road segment before reaching the road segment with which an update is associated, the server may distribute the updates or updated model to the autonomous vehicle before the vehicle reaches the road segment.

In some embodiments, the remote server may collect trajectories and landmarks from multiple clients (e.g., vehicles that travel along a common road segment). The server may match curves using landmarks and create an average road model based on the trajectories collected from the multiple vehicles. The server may also compute a graph of roads and the most probable path at each node or conjunction of the road segment. For example, the remote server may align the trajectories to generate a crowdsourced sparse map from the collected trajectories.

The server may average landmark properties received from multiple vehicles that travelled along the common road segment, such as the distances between one landmark to another (e.g., a previous one along the road segment) as measured by multiple vehicles, to determine an arc-length parameter and support localization along the path and speed calibration for each client vehicle. The server may average the physical dimensions of a landmark measured by multiple vehicles travelled along the common road segment and recognized the same landmark. The averaged physical dimensions may be used to support distance estimation, such as the distance from the vehicle to the landmark. The server may average lateral positions of a landmark (e.g., position from the lane in which vehicles are travelling in to the landmark) measured by multiple vehicles travelled along the common road segment and recognized the same landmark. The averaged lateral portion may be used to support lane assignment. The server may average the GPS coordinates of the landmark measured by multiple vehicles travelled along the same road segment and recognized the same landmark. The averaged GPS coordinates of the landmark may be used to support global localization or positioning of the landmark in the road model.

In some embodiments, the server may identify model changes, such as constructions, detours, new signs, removal of signs, etc., based on data received from the vehicles. The server may continuously or periodically or instantaneously update the model upon receiving new data from the vehicles. The server may distribute updates to the model or the updated model to vehicles for providing autonomous navigation. For example, as discussed further below, the server may use crowdsourced data to filter out “ghost” landmarks detected by vehicles.

In some embodiments, the server may analyze driver interventions during the autonomous driving. The server may analyze data received from the vehicle at the time and location where intervention occurs, and/or data received prior to the time the intervention occurred. The server may identify certain portions of the data that caused or are closely related to the intervention, for example, data indicating a temporary lane closure setup, data indicating a pedestrian in the road. The server may update the model based on the identified data. For example, the server may modify one or more trajectories stored in the model.

FIG. 12 is a schematic illustration of a system that uses crowdsourcing to generate a sparse map (as well as distribute and navigate using a crowdsourced sparse map). FIG. 12 shows a road segment 1200 that includes one or more lanes. A plurality of vehicles 1205, 1210, 1215, 1220, and 1225 may travel on road segment 1200 at the same time or at different times (although shown as appearing on road segment 1200 at the same time in FIG. 12 ). At least one of vehicles 1205, 1210, 1215, 1220, and 1225 may be an autonomous vehicle. For simplicity of the present example, all of the vehicles 1205, 1210, 1215, 1220, and 1225 are presumed to be autonomous vehicles.

Each vehicle may be similar to vehicles disclosed in other embodiments (e.g., vehicle 200), and may include components or devices included in or associated with vehicles disclosed in other embodiments. Each vehicle may be equipped with an image capture device or camera (e.g., image capture device 122 or camera 122). Each vehicle may communicate with a remote server 1230 via one or more networks (e.g., over a cellular network and/or the Internet, etc.) through wireless communication paths 1235, as indicated by the dashed lines. Each vehicle may transmit data to server 1230 and receive data from server 1230. For example, server 1230 may collect data from multiple vehicles travelling on the road segment 1200 at different times, and may process the collected data to generate an autonomous vehicle road navigation model, or an update to the model. Server 1230 may transmit the autonomous vehicle road navigation model or the update to the model to the vehicles that transmitted data to server 1230. Server 1230 may transmit the autonomous vehicle road navigation model or the update to the model to other vehicles that travel on road segment 1200 at later times.

As vehicles 1205, 1210, 1215, 1220, and 1225 travel on road segment 1200, navigation information collected (e.g., detected, sensed, or measured) by vehicles 1205, 1210, 1215, 1220, and 1225 may be transmitted to server 1230. In some embodiments, the navigation information may be associated with the common road segment 1200. The navigation information may include a trajectory associated with each of the vehicles 1205, 1210, 1215, 1220, and 1225 as each vehicle travels over road segment 1200. In some embodiments, the trajectory may be reconstructed based on data sensed by various sensors and devices provided on vehicle 1205. For example, the trajectory may be reconstructed based on at least one of accelerometer data, speed data, landmarks data, road geometry or profile data, vehicle positioning data, and ego motion data. In some embodiments, the trajectory may be reconstructed based on data from inertial sensors, such as accelerometer, and the velocity of vehicle 1205 sensed by a speed sensor. In addition, in some embodiments, the trajectory may be determined (e.g., by a processor onboard each of vehicles 1205, 1210, 1215, 1220, and 1225) based on sensed ego motion of the camera, which may indicate three dimensional translation and/or three dimensional rotations (or rotational motions). The ego motion of the camera (and hence the vehicle body) may be determined from analysis of one or more images captured by the camera.

In some embodiments, the trajectory of vehicle 1205 may be determined by a processor provided aboard vehicle 1205 and transmitted to server 1230. In other embodiments, server 1230 may receive data sensed by the various sensors and devices provided in vehicle 1205, and determine the trajectory based on the data received from vehicle 1205.

In some embodiments, the navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 to server 1230 may include data regarding the road surface, the road geometry, or the road profile. The geometry of road segment 1200 may include lane structure and/or landmarks. The lane structure may include the total number of lanes of road segment 1200, the type of lanes (e.g., one-way lane, two-way lane, driving lane, passing lane, etc.), markings on lanes, width of lanes, etc. In some embodiments, the navigation information may include a lane assignment, e.g., which lane of a plurality of lanes a vehicle is traveling in. For example, the lane assignment may be associated with a numerical value “3” indicating that the vehicle is traveling on the third lane from the left or right. As another example, the lane assignment may be associated with a text value “center lane” indicating the vehicle is traveling on the center lane.

Server 1230 may store the navigation information on a non-transitory computer-readable medium, such as a hard drive, a compact disc, a tape, a memory, etc. Server 1230 may generate (e.g., through a processor included in server 1230) at least a portion of an autonomous vehicle road navigation model for the common road segment 1200 based on the navigation information received from the plurality of vehicles 1205, 1210, 1215, 1220, and 1225 and may store the model as a portion of a sparse map. Server 1230 may determine a trajectory associated with each lane based on crowdsourced data (e.g., navigation information) received from multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) that travel on a lane of road segment at different times. Server 1230 may generate the autonomous vehicle road navigation model or a portion of the model (e.g., an updated portion) based on a plurality of trajectories determined based on the crowd sourced navigation data. Server 1230 may transmit the model or the updated portion of the model to one or more of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 traveling on road segment 1200 or any other autonomous vehicles that travel on road segment at a later time for updating an existing autonomous vehicle road navigation model provided in a navigation system of the vehicles. The autonomous vehicle road navigation model may be used by the autonomous vehicles in autonomously navigating along the common road segment 1200.

As explained above, the autonomous vehicle road navigation model may be included in a sparse map (e.g., sparse map 800 depicted in FIG. 8 ). Sparse map 800 may include sparse recording of data related to road geometry and/or landmarks along a road, which may provide sufficient information for guiding autonomous navigation of an autonomous vehicle, yet does not require excessive data storage. In some embodiments, the autonomous vehicle road navigation model may be stored separately from sparse map 800, and may use map data from sparse map 800 when the model is executed for navigation. In some embodiments, the autonomous vehicle road navigation model may use map data included in sparse map 800 for determining target trajectories along road segment 1200 for guiding autonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 or other vehicles that later travel along road segment 1200. For example, when the autonomous vehicle road navigation model is executed by a processor included in a navigation system of vehicle 1205, the model may cause the processor to compare the trajectories determined based on the navigation information received from vehicle 1205 with predetermined trajectories included in sparse map 800 to validate and/or correct the current traveling course of vehicle 1205.

In the autonomous vehicle road navigation model, the geometry of a road feature or target trajectory may be encoded by a curve in a three-dimensional space. In one embodiment, the curve may be a three dimensional spline including one or more connecting three dimensional polynomials. As one of skill in the art would understand, a spline may be a numerical function that is piece-wise defined by a series of polynomials for fitting data. A spline for fitting the three dimensional geometry data of the road may include a linear spline (first order), a quadratic spline (second order), a cubic spline (third order), or any other splines (other orders), or a combination thereof. The spline may include one or more three dimensional polynomials of different orders connecting (e.g., fitting) data points of the three dimensional geometry data of the road. In some embodiments, the autonomous vehicle road navigation model may include a three dimensional spline corresponding to a target trajectory along a common road segment (e.g., road segment 1200) or a lane of the road segment 1200.

As explained above, the autonomous vehicle road navigation model included in the sparse map may include other information, such as identification of at least one landmark along road segment 1200. The landmark may be visible within a field of view of a camera (e.g., camera 122) installed on each of vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, camera 122 may capture an image of a landmark. A processor (e.g., processor 180, 190, or processing unit 110) provided on vehicle 1205 may process the image of the landmark to extract identification information for the landmark. The landmark identification information, rather than an actual image of the landmark, may be stored in sparse map 800. The landmark identification information may require much less storage space than an actual image. Other sensors or systems (e.g., GPS system) may also provide certain identification information of the landmark (e.g., position of landmark). The landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign (e.g., a highway exit sign with an arrow indicating a direction, a highway sign with arrows pointing to different directions or places), a landmark beacon, or a lamppost. A landmark beacon refers to a device (e.g., an RFID device) installed along a road segment that transmits or reflects a signal to a receiver installed on a vehicle, such that when the vehicle passes by the device, the beacon received by the vehicle and the location of the device (e.g., determined from GPS location of the device) may be used as a landmark to be included in the autonomous vehicle road navigation model and/or the sparse map 800.

The identification of at least one landmark may include a position of the at least one landmark. The position of the landmark may be determined based on position measurements performed using sensor systems (e.g., Global Positioning Systems, inertial based positioning systems, landmark beacon, etc.) associated with the plurality of vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the position of the landmark may be determined by averaging the position measurements detected, collected, or received by sensor systems on different vehicles 1205, 1210, 1215, 1220, and 1225 through multiple drives. For example, vehicles 1205, 1210, 1215, 1220, and 1225 may transmit position measurements data to server 1230, which may average the position measurements and use the averaged position measurement as the position of the landmark. The position of the landmark may be continuously refined by measurements received from vehicles in subsequent drives.

The identification of the landmark may include a size of the landmark. The processor provided on a vehicle (e.g., 1205) may estimate the physical size of the landmark based on the analysis of the images. Server 1230 may receive multiple estimates of the physical size of the same landmark from different vehicles over different drives. Server 1230 may average the different estimates to arrive at a physical size for the landmark, and store that landmark size in the road model. The physical size estimate may be used to further determine or estimate a distance from the vehicle to the landmark. The distance to the landmark may be estimated based on the current speed of the vehicle and a scale of expansion based on the position of the landmark appearing in the images relative to the focus of expansion of the camera. For example, the distance to landmark may be estimated by Z=V*dt*R/D, where V is the speed of vehicle, R is the distance in the image from the landmark at time t1 to the focus of expansion, and D is the change in distance for the landmark in the image from t1 to t2. dt represents the (t2−t1). For example, the distance to landmark may be estimated by Z=V*dt*R/D, where V is the speed of vehicle, R is the distance in the image between the landmark and the focus of expansion, dt is a time interval, and D is the image displacement of the landmark along the epipolar line. Other equations equivalent to the above equation, such as Z=V*ω/Δω, may be used for estimating the distance to the landmark. Here, V is the vehicle speed, ω is an image length (like the object width), and Δω is the change of that image length in a unit of time.

When the physical size of the landmark is known, the distance to the landmark may also be determined based on the following equation: Z=f*W/ω, where f is the focal length, W is the size of the landmark (e.g., height or width), ω is the number of pixels when the landmark leaves the image. From the above equation, a change in distance Z may be calculated using ΔZ=f*W*Δω/ω²+f*ΔW/ω, where ΔW decays to zero by averaging, and where Δω is the number of pixels representing a bounding box accuracy in the image. A value estimating the physical size of the landmark may be calculated by averaging multiple observations at the server side. The resulting error in distance estimation may be very small. There are two sources of error that may occur when using the formula above, namely ΔW and Δw. Their contribution to the distance error is given by ΔZ=f*W*Δω/ω²+f*ΔW/ω. However, ΔW decays to zero by averaging; hence ΔZ is determined by Δω (e.g., the inaccuracy of the bounding box in the image).

For landmarks of unknown dimensions, the distance to the landmark may be estimated by tracking feature points on the landmark between successive frames. For example, certain features appearing on a speed limit sign may be tracked between two or more image frames. Based on these tracked features, a distance distribution per feature point may be generated. The distance estimate may be extracted from the distance distribution. For example, the most frequent distance appearing in the distance distribution may be used as the distance estimate. As another example, the average of the distance distribution may be used as the distance estimate.

FIG. 13 illustrates an example autonomous vehicle road navigation model represented by a plurality of three dimensional splines 1301, 1302, and 1303. The curves 1301, 1302, and 1303 shown in FIG. 13 are for illustration purpose only. Each spline may include one or more three dimensional polynomials connecting a plurality of data points 1310. Each polynomial may be a first order polynomial, a second order polynomial, a third order polynomial, or a combination of any suitable polynomials having different orders. Each data point 1310 may be associated with the navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, each data point 1310 may be associated with data related to landmarks (e.g., size, location, and identification information of landmarks) and/or road signature profiles (e.g., road geometry, road roughness profile, road curvature profile, road width profile). In some embodiments, some data points 1310 may be associated with data related to landmarks, and others may be associated with data related to road signature profiles.

FIG. 14 illustrates raw location data 1410 (e.g., GPS data) received from five separate drives. One drive may be separate from another drive if it was traversed by separate vehicles at the same time, by the same vehicle at separate times, or by separate vehicles at separate times. To account for errors in the location data 1410 and for differing locations of vehicles within the same lane (e.g., one vehicle may drive closer to the left of a lane than another), server 1230 may generate a map skeleton 1420 using one or more statistical techniques to determine whether variations in the raw location data 1410 represent actual divergences or statistical errors. Each path within skeleton 1420 may be linked back to the raw data 1410 that formed the path. For example, the path between A and B within skeleton 1420 is linked to raw data 1410 from drives 2, 3, 4, and 5 but not from drive 1. Skeleton 1420 may not be detailed enough to be used to navigate a vehicle (e.g., because it combines drives from multiple lanes on the same road unlike the splines described above) but may provide useful topological information and may be used to define intersections.

FIG. 15 illustrates an example by which additional detail may be generated for a sparse map within a segment of a map skeleton (e.g., segment A to B within skeleton 1420). As depicted in FIG. 15 , the data (e.g. ego-motion data, road markings data, and the like) may be shown as a function of position S (or S₁ or S₂) along the drive. Server 1230 may identify landmarks for the sparse map by identifying unique matches between landmarks 1501, 1503, and 1505 of drive 1510 and landmarks 1507 and 1509 of drive 1520. Such a matching algorithm may result in identification of landmarks 1511, 1513, and 1515. One skilled in the art would recognize, however, that other matching algorithms may be used. For example, probability optimization may be used in lieu of or in combination with unique matching. Server 1230 may longitudinally align the drives to align the matched landmarks. For example, server 1230 may select one drive (e.g., drive 1520) as a reference drive and then shift and/or elastically stretch the other drive(s) (e.g., drive 1510) for alignment.

FIG. 16 shows an example of aligned landmark data for use in a sparse map. In the example of FIG. 16 , landmark 1610 comprises a road sign. The example of FIG. 16 further depicts data from a plurality of drives 1601, 1603, 1605, 1607, 1609, 1611, and 1613. In the example of FIG. 16 , the data from drive 1613 consists of a “ghost” landmark, and the server 1230 may identify it as such because none of drives 1601, 1603, 1605, 1607, 1609, and 1611 include an identification of a landmark in the vicinity of the identified landmark in drive 1613. Accordingly, server 1230 may accept potential landmarks when a ratio of images in which the landmark does appear to images in which the landmark does not appear exceeds a threshold and/or may reject potential landmarks when a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.

FIG. 17 depicts a system 1700 for generating drive data, which may be used to crowdsource a sparse map. As depicted in FIG. 17 , system 1700 may include a camera 1701 and a locating device 1703 (e.g., a GPS locator). Camera 1701 and locating device 1703 may be mounted on a vehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). Camera 1701 may produce a plurality of data of multiple types, e.g., ego motion data, traffic sign data, road data, or the like. The camera data and location data may be segmented into drive segments 1705. For example, drive segments 1705 may each have camera data and location data from less than 1 km of driving.

In some embodiments, system 1700 may remove redundancies in drive segments 1705. For example, if a landmark appears in multiple images from camera 1701, system 1700 may strip the redundant data such that the drive segments 1705 only contain one copy of the location of and any metadata relating to the landmark. By way of further example, if a lane marking appears in multiple images from camera 1701, system 1700 may strip the redundant data such that the drive segments 1705 only contain one copy of the location of and any metadata relating to the lane marking.

System 1700 also includes a server (e.g., server 1230). Server 1230 may receive drive segments 1705 from the vehicle and recombine the drive segments 1705 into a single drive 1707. Such an arrangement may allow for reduce bandwidth requirements when transferring data between the vehicle and the server while also allowing for the server to store data relating to an entire drive.

FIG. 18 depicts system 1700 of FIG. 17 further configured for crowdsourcing a sparse map. As in FIG. 17 , system 1700 includes vehicle 1810, which captures drive data using, for example, a camera (which produces, e.g., ego motion data, traffic sign data, road data, or the like) and a locating device (e.g., a GPS locator). As in FIG. 17 , vehicle 1810 segments the collected data into drive segments (depicted as “DS1 1,” “DS2 1,” “DSN 1” in FIG. 18 ). Server 1230 then receives the drive segments and reconstructs a drive (depicted as “Drive 1” in FIG. 18 ) from the received segments.

As further depicted in FIG. 18 , system 1700 also receives data from additional vehicles. For example, vehicle 1820 also captures drive data using, for example, a camera (which produces, e.g., ego motion data, traffic sign data, road data, or the like) and a locating device (e.g., a GPS locator). Similar to vehicle 1810, vehicle 1820 segments the collected data into drive segments (depicted as “DS1 2,” “DS2 2,” “DSN 2” in FIG. 18 ). Server 1230 then receives the drive segments and reconstructs a drive (depicted as “Drive 2” in FIG. 18 ) from the received segments. Any number of additional vehicles may be used. For example, FIG. 18 also includes “CAR N” that captures drive data, segments it into drive segments (depicted as “DS1 N,” “DS2 N,” “DSN N” in FIG. 18 ), and sends it to server 1230 for reconstruction into a drive (depicted as “Drive N” in FIG. 18 ).

As depicted in FIG. 18 , server 1230 may construct a sparse map (depicted as “MAP”) using the reconstructed drives (e.g., “Drive 1,” “Drive 2,” and “Drive N”) collected from a plurality of vehicles (e.g., “CAR 1” (also labeled vehicle 1810), “CAR 2” (also labeled vehicle 1820), and “CAR N”).

FIG. 19 is a flowchart showing an example process 1900 for generating a sparse map for autonomous vehicle navigation along a road segment. Process 1900 may be performed by one or more processing devices included in server 1230.

Process 1900 may include receiving a plurality of images acquired as one or more vehicles traverse the road segment (step 1905). Server 1230 may receive images from cameras included within one or more of vehicles 1205, 1210, 1215, 1220, and 1225. For example, camera 122 may capture one or more images of the environment surrounding vehicle 1205 as vehicle 1205 travels along road segment 1200. In some embodiments, server 1230 may also receive stripped down image data that has had redundancies removed by a processor on vehicle 1205, as discussed above with respect to FIG. 17 .

Process 1900 may further include identifying, based on the plurality of images, at least one line representation of a road surface feature extending along the road segment (step 1910). Each line representation may represent a path along the road segment substantially corresponding with the road surface feature. For example, server 1230 may analyze the environmental images received from camera 122 to identify a road edge or a lane marking and determine a trajectory of travel along road segment 1200 associated with the road edge or lane marking. In some embodiments, the trajectory (or line representation) may include a spline, a polynomial representation, or a curve. Server 1230 may determine the trajectory of travel of vehicle 1205 based on camera ego motions (e.g., three dimensional translation and/or three dimensional rotational motions) received at step 1905.

Process 1900 may also include identifying, based on the plurality of images, a plurality of landmarks associated with the road segment (step 1910). For example, server 1230 may analyze the environmental images received from camera 122 to identify one or more landmarks, such as road sign along road segment 1200. Server 1230 may identify the landmarks using analysis of the plurality of images acquired as one or more vehicles traverse the road segment. To enable crowdsourcing, the analysis may include rules regarding accepting and rejecting possible landmarks associated with the road segment. For example, the analysis may include accepting potential landmarks when a ratio of images in which the landmark does appear to images in which the landmark does not appear exceeds a threshold and/or rejecting potential landmarks when a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.

Process 1900 may include other operations or steps performed by server 1230. For example, the navigation information may include a target trajectory for vehicles to travel along a road segment, and process 1900 may include clustering, by server 1230, vehicle trajectories related to multiple vehicles travelling on the road segment and determining the target trajectory based on the clustered vehicle trajectories, as discussed in further detail below. Clustering vehicle trajectories may include clustering, by server 1230, the multiple trajectories related to the vehicles travelling on the road segment into a plurality of clusters based on at least one of the absolute heading of vehicles or lane assignment of the vehicles. Generating the target trajectory may include averaging, by server 1230, the clustered trajectories. By way of further example, process 1900 may include aligning data received in step 1905. Other processes or steps performed by server 1230, as described above, may also be included in process 1900.

The disclosed systems and methods may include other features. For example, the disclosed systems may use local coordinates, rather than global coordinates. For autonomous driving, some systems may present data in world coordinates. For example, longitude and latitude coordinates on the earth surface may be used. In order to use the map for steering, the host vehicle may determine its position and orientation relative to the map. It seems natural to use a GPS device on board, in order to position the vehicle on the map and in order to find the rotation transformation between the body reference frame and the world reference frame (e.g., North, East and Down). Once the body reference frame is aligned with the map reference frame, then the desired route may be expressed in the body reference frame and the steering commands may be computed or generated.

The disclosed systems and methods may enable autonomous vehicle navigation (e.g., steering control) with low footprint models, which may be collected by the autonomous vehicles themselves without the aid of expensive surveying equipment. To support the autonomous navigation (e.g., steering applications), the road model may include a sparse map having the geometry of the road, its lane structure, and landmarks that may be used to determine the location or position of vehicles along a trajectory included in the model. As discussed above, generation of the sparse map may be performed by a remote server that communicates with vehicles travelling on the road and that receives data from the vehicles. The data may include sensed data, trajectories reconstructed based on the sensed data, and/or recommended trajectories that may represent modified reconstructed trajectories. As discussed below, the server may transmit the model back to the vehicles or other vehicles that later travel on the road to aid in autonomous navigation.

FIG. 20 illustrates a block diagram of server 1230. Server 1230 may include a communication unit 2005, which may include both hardware components (e.g., communication control circuits, switches, and antenna), and software components (e.g., communication protocols, computer codes). For example, communication unit 2005 may include at least one network interface. Server 1230 may communicate with vehicles 1205, 1210, 1215, 1220, and 1225 through communication unit 2005. For example, server 1230 may receive, through communication unit 2005, navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225. Server 1230 may distribute, through communication unit 2005, the autonomous vehicle road navigation model to one or more autonomous vehicles.

Server 1230 may include at least one non-transitory storage medium 2010, such as a hard drive, a compact disc, a tape, etc. Storage device 1410 may be configured to store data, such as navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225 and/or the autonomous vehicle road navigation model that server 1230 generates based on the navigation information. Storage device 2010 may be configured to store any other information, such as a sparse map (e.g., sparse map 800 discussed above with respect to FIG. 8 ).

In addition to or in place of storage device 2010, server 1230 may include a memory 2015. Memory 2015 may be similar to or different from memory 140 or 150. Memory 2015 may be a non-transitory memory, such as a flash memory, a random access memory, etc. Memory 2015 may be configured to store data, such as computer codes or instructions executable by a processor (e.g., processor 2020), map data (e.g., data of sparse map 800), the autonomous vehicle road navigation model, and/or navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225.

Server 1230 may include at least one processing device 2020 configured to execute computer codes or instructions stored in memory 2015 to perform various functions. For example, processing device 2020 may analyze the navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225, and generate the autonomous vehicle road navigation model based on the analysis. Processing device 2020 may control communication unit 1405 to distribute the autonomous vehicle road navigation model to one or more autonomous vehicles (e.g., one or more of vehicles 1205, 1210, 1215, 1220, and 1225 or any vehicle that travels on road segment 1200 at a later time). Processing device 2020 may be similar to or different from processor 180, 190, or processing unit 110.

FIG. 21 illustrates a block diagram of memory 2015, which may store computer code or instructions for performing one or more operations for generating a road navigation model for use in autonomous vehicle navigation. As shown in FIG. 21 , memory 2015 may store one or more modules for performing the operations for processing vehicle navigation information. For example, memory 2015 may include a model generating module 2105 and a model distributing module 2110. Processor 2020 may execute the instructions stored in any of modules 2105 and 2110 included in memory 2015.

Model generating module 2105 may store instructions which, when executed by processor 2020, may generate at least a portion of an autonomous vehicle road navigation model for a common road segment (e.g., road segment 1200) based on navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. For example, in generating the autonomous vehicle road navigation model, processor 2020 may cluster vehicle trajectories along the common road segment 1200 into different clusters. Processor 2020 may determine a target trajectory along the common road segment 1200 based on the clustered vehicle trajectories for each of the different clusters. Such an operation may include finding a mean or average trajectory of the clustered vehicle trajectories (e.g., by averaging data representing the clustered vehicle trajectories) in each cluster. In some embodiments, the target trajectory may be associated with a single lane of the common road segment 1200.

The road model and/or sparse map may store trajectories associated with a road segment. These trajectories may be referred to as target trajectories, which are provided to autonomous vehicles for autonomous navigation. The target trajectories may be received from multiple vehicles, or may be generated based on actual trajectories or recommended trajectories (actual trajectories with some modifications) received from multiple vehicles. The target trajectories included in the road model or sparse map may be continuously updated (e.g., averaged) with new trajectories received from other vehicles.

Vehicles travelling on a road segment may collect data by various sensors. The data may include landmarks, road signature profile, vehicle motion (e.g., accelerometer data, speed data), vehicle position (e.g., GPS data), and may either reconstruct the actual trajectories themselves, or transmit the data to a server, which will reconstruct the actual trajectories for the vehicles. In some embodiments, the vehicles may transmit data relating to a trajectory (e.g., a curve in an arbitrary reference frame), landmarks data, and lane assignment along traveling path to server 1230. Various vehicles travelling along the same road segment at multiple drives may have different trajectories. Server 1230 may identify routes or trajectories associated with each lane from the trajectories received from vehicles through a clustering process.

FIG. 22 illustrates a process of clustering vehicle trajectories associated with vehicles 1205, 1210, 1215, 1220, and 1225 for determining a target trajectory for the common road segment (e.g., road segment 1200). The target trajectory or a plurality of target trajectories determined from the clustering process may be included in the autonomous vehicle road navigation model or sparse map 800. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 traveling along road segment 1200 may transmit a plurality of trajectories 2200 to server 1230. In some embodiments, server 1230 may generate trajectories based on landmark, road geometry, and vehicle motion information received from vehicles 1205, 1210, 1215, 1220, and 1225. To generate the autonomous vehicle road navigation model, server 1230 may cluster vehicle trajectories 1600 into a plurality of clusters 2205, 2210, 2215, 2220, 2225, and 2230, as shown in FIG. 22 .

Clustering may be performed using various criteria. In some embodiments, all drives in a cluster may be similar with respect to the absolute heading along the road segment 1200. The absolute heading may be obtained from GPS signals received by vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the absolute heading may be obtained using dead reckoning Dead reckoning, as one of skill in the art would understand, may be used to determine the current position and hence heading of vehicles 1205, 1210, 1215, 1220, and 1225 by using previously determined position, estimated speed, etc. Trajectories clustered by absolute heading may be useful for identifying routes along the roadways.

In some embodiments, all the drives in a cluster may be similar with respect to the lane assignment (e.g., in the same lane before and after a junction) along the drive on road segment 1200. Trajectories clustered by lane assignment may be useful for identifying lanes along the roadways. In some embodiments, both criteria (e.g., absolute heading and lane assignment) may be used for clustering.

In each cluster 2205, 2210, 2215, 2220, 2225, and 2230, trajectories may be averaged to obtain a target trajectory associated with the specific cluster. For example, the trajectories from multiple drives associated with the same lane cluster may be averaged. The averaged trajectory may be a target trajectory associate with a specific lane. To average a cluster of trajectories, server 1230 may select a reference frame of an arbitrary trajectory C0. For all other trajectories (C1, . . . , Cn), server 1230 may find a rigid transformation that maps Ci to C0, where i=1, 2, . . . , n, where n is a positive integer number, corresponding to the total number of trajectories included in the cluster. Server 1230 may compute a mean curve or trajectory in the C0 reference frame.

In some embodiments, the landmarks may define an arc length matching between different drives, which may be used for alignment of trajectories with lanes. In some embodiments, lane marks before and after a junction may be used for alignment of trajectories with lanes.

To assemble lanes from the trajectories, server 1230 may select a reference frame of an arbitrary lane. Server 1230 may map partially overlapping lanes to the selected reference frame. Server 1230 may continue mapping until all lanes are in the same reference frame. Lanes that are next to each other may be aligned as if they were the same lane, and later they may be shifted laterally.

Landmarks recognized along the road segment may be mapped to the common reference frame, first at the lane level, then at the junction level. For example, the same landmarks may be recognized multiple times by multiple vehicles in multiple drives. The data regarding the same landmarks received in different drives may be slightly different. Such data may be averaged and mapped to the same reference frame, such as the C0 reference frame. Additionally or alternatively, the variance of the data of the same landmark received in multiple drives may be calculated.

In some embodiments, each lane of road segment 120 may be associated with a target trajectory and certain landmarks. The target trajectory or a plurality of such target trajectories may be included in the autonomous vehicle road navigation model, which may be used later by other autonomous vehicles travelling along the same road segment 1200. Landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 while the vehicles travel along road segment 1200 may be recorded in association with the target trajectory. The data of the target trajectories and landmarks may be continuously or periodically updated with new data received from other vehicles in subsequent drives.

For localization of an autonomous vehicle, the disclosed systems and methods may use an Extended Kalman Filter. The location of the vehicle may be determined based on three dimensional position data and/or three dimensional orientation data, prediction of future location ahead of vehicle's current location by integration of ego motion. The localization of vehicle may be corrected or adjusted by image observations of landmarks. For example, when vehicle detects a landmark within an image captured by the camera, the landmark may be compared to a known landmark stored within the road model or sparse map 800. The known landmark may have a known location (e.g., GPS data) along a target trajectory stored in the road model and/or sparse map 800. Based on the current speed and images of the landmark, the distance from the vehicle to the landmark may be estimated. The location of the vehicle along a target trajectory may be adjusted based on the distance to the landmark and the landmark's known location (stored in the road model or sparse map 800). The landmark's position/location data (e.g., mean values from multiple drives) stored in the road model and/or sparse map 800 may be presumed to be accurate.

In some embodiments, the disclosed system may form a closed loop subsystem, in which estimation of the vehicle six degrees of freedom location (e.g., three dimensional position data plus three dimensional orientation data) may be used for navigating (e.g., steering the wheel of) the autonomous vehicle to reach a desired point (e.g., 1.3 second ahead in the stored). In turn, data measured from the steering and actual navigation may be used to estimate the six degrees of freedom location.

In some embodiments, poles along a road, such as lampposts and power or cable line poles may be used as landmarks for localizing the vehicles. Other landmarks such as traffic signs, traffic lights, arrows on the road, stop lines, as well as static features or signatures of an object along the road segment may also be used as landmarks for localizing the vehicle. When poles are used for localization, the x observation of the poles (i.e., the viewing angle from the vehicle) may be used, rather than the y observation (i.e., the distance to the pole) since the bottoms of the poles may be occluded and sometimes they are not on the road plane.

FIG. 23 illustrates a navigation system for a vehicle, which may be used for autonomous navigation using a crowdsourced sparse map. For illustration, the vehicle is referenced as vehicle 1205. The vehicle shown in FIG. 23 may be any other vehicle disclosed herein, including, for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200 shown in other embodiments. As shown in FIG. 12 , vehicle 1205 may communicate with server 1230. Vehicle 1205 may include an image capture device 122 (e.g., camera 122). Vehicle 1205 may include a navigation system 2300 configured for providing navigation guidance for vehicle 1205 to travel on a road (e.g., road segment 1200). Vehicle 1205 may also include other sensors, such as a speed sensor 2320 and an accelerometer 2325. Speed sensor 2320 may be configured to detect the speed of vehicle 1205. Accelerometer 2325 may be configured to detect an acceleration or deceleration of vehicle 1205. Vehicle 1205 shown in FIG. 23 may be an autonomous vehicle, and the navigation system 2300 may be used for providing navigation guidance for autonomous driving. Alternatively, vehicle 1205 may also be a non-autonomous, human-controlled vehicle, and navigation system 2300 may still be used for providing navigation guidance.

Navigation system 2300 may include a communication unit 2305 configured to communicate with server 1230 through communication path 1235. Navigation system 2300 may also include a GPS unit 2310 configured to receive and process GPS signals. Navigation system 2300 may further include at least one processor 2315 configured to process data, such as GPS signals, map data from sparse map 800 (which may be stored on a storage device provided onboard vehicle 1205 and/or received from server 1230), road geometry sensed by a road profile sensor 2330, images captured by camera 122, and/or autonomous vehicle road navigation model received from server 1230. The road profile sensor 2330 may include different types of devices for measuring different types of road profile, such as road surface roughness, road width, road elevation, road curvature, etc. For example, the road profile sensor 2330 may include a device that measures the motion of a suspension of vehicle 2305 to derive the road roughness profile. In some embodiments, the road profile sensor 2330 may include radar sensors to measure the distance from vehicle 1205 to road sides (e.g., barrier on the road sides), thereby measuring the width of the road. In some embodiments, the road profile sensor 2330 may include a device configured for measuring the up and down elevation of the road. In some embodiment, the road profile sensor 2330 may include a device configured to measure the road curvature. For example, a camera (e.g., camera 122 or another camera) may be used to capture images of the road showing road curvatures. Vehicle 1205 may use such images to detect road curvatures.

The at least one processor 2315 may be programmed to receive, from camera 122, at least one environmental image associated with vehicle 1205. The at least one processor 2315 may analyze the at least one environmental image to determine navigation information related to the vehicle 1205. The navigation information may include a trajectory related to the travel of vehicle 1205 along road segment 1200. The at least one processor 2315 may determine the trajectory based on motions of camera 122 (and hence the vehicle), such as three dimensional translation and three dimensional rotational motions. In some embodiments, the at least one processor 2315 may determine the translation and rotational motions of camera 122 based on analysis of a plurality of images acquired by camera 122. In some embodiments, the navigation information may include lane assignment information (e.g., in which lane vehicle 1205 is travelling along road segment 1200). The navigation information transmitted from vehicle 1205 to server 1230 may be used by server 1230 to generate and/or update an autonomous vehicle road navigation model, which may be transmitted back from server 1230 to vehicle 1205 for providing autonomous navigation guidance for vehicle 1205.

The at least one processor 2315 may also be programmed to transmit the navigation information from vehicle 1205 to server 1230. In some embodiments, the navigation information may be transmitted to server 1230 along with road information. The road location information may include at least one of the GPS signal received by the GPS unit 2310, landmark information, road geometry, lane information, etc. The at least one processor 2315 may receive, from server 1230, the autonomous vehicle road navigation model or a portion of the model. The autonomous vehicle road navigation model received from server 1230 may include at least one update based on the navigation information transmitted from vehicle 1205 to server 1230. The portion of the model transmitted from server 1230 to vehicle 1205 may include an updated portion of the model. The at least one processor 2315 may cause at least one navigational maneuver (e.g., steering such as making a turn, braking, accelerating, passing another vehicle, etc.) by vehicle 1205 based on the received autonomous vehicle road navigation model or the updated portion of the model.

The at least one processor 2315 may be configured to communicate with various sensors and components included in vehicle 1205, including communication unit 1705, GPS unit 2315, camera 122, speed sensor 2320, accelerometer 2325, and road profile sensor 2330. The at least one processor 2315 may collect information or data from various sensors and components, and transmit the information or data to server 1230 through communication unit 2305. Alternatively or additionally, various sensors or components of vehicle 1205 may also communicate with server 1230 and transmit data or information collected by the sensors or components to server 1230.

In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 may communicate with each other, and may share navigation information with each other, such that at least one of the vehicles 1205, 1210, 1215, 1220, and 1225 may generate the autonomous vehicle road navigation model using crowdsourcing, e.g., based on information shared by other vehicles. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 may share navigation information with each other and each vehicle may update its own the autonomous vehicle road navigation model provided in the vehicle. In some embodiments, at least one of the vehicles 1205, 1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may function as a hub vehicle. The at least one processor 2315 of the hub vehicle (e.g., vehicle 1205) may perform some or all of the functions performed by server 1230. For example, the at least one processor 2315 of the hub vehicle may communicate with other vehicles and receive navigation information from other vehicles. The at least one processor 2315 of the hub vehicle may generate the autonomous vehicle road navigation model or an update to the model based on the shared information received from other vehicles. The at least one processor 2315 of the hub vehicle may transmit the autonomous vehicle road navigation model or the update to the model to other vehicles for providing autonomous navigation guidance.

Navigation Based on Sparse Maps

As previously discussed, the autonomous vehicle road navigation model including sparse map 800 may include a plurality of mapped lane marks and a plurality of mapped objects/features associated with a road segment. As discussed in greater detail below, these mapped lane marks, objects, and features may be used when the autonomous vehicle navigates. For example, in some embodiments, the mapped objects and features may be used to localized a host vehicle relative to the map (e.g., relative to a mapped target trajectory). The mapped lane marks may be used (e.g., as a check) to determine a lateral position and/or orientation relative to a planned or target trajectory. With this position information, the autonomous vehicle may be able to adjust a heading direction to match a direction of a target trajectory at the determined position.

Vehicle 200 may be configured to detect lane marks in a given road segment. The road segment may include any markings on a road for guiding vehicle traffic on a roadway. For example, the lane marks may be continuous or dashed lines demarking the edge of a lane of travel. The lane marks may also include double lines, such as a double continuous lines, double dashed lines or a combination of continuous and dashed lines indicating, for example, whether passing is permitted in an adjacent lane. The lane marks may also include freeway entrance and exit markings indicating, for example, a deceleration lane for an exit ramp or dotted lines indicating that a lane is turn-only or that the lane is ending. The markings may further indicate a work zone, a temporary lane shift, a path of travel through an intersection, a median, a special purpose lane (e.g., a bike lane, HOV lane, etc.), or other miscellaneous markings (e.g., crosswalk, a speed hump, a railway crossing, a stop line, etc.).

Vehicle 200 may use cameras, such as image capture devices 122 and 124 included in image acquisition unit 120, to capture images of the surrounding lane marks. Vehicle 200 may analyze the images to detect point locations associated with the lane marks based on features identified within one or more of the captured images. These point locations may be uploaded to a server to represent the lane marks in sparse map 800. Depending on the position and field of view of the camera, lane marks may be detected for both sides of the vehicle simultaneously from a single image. In other embodiments, different cameras may be used to capture images on multiple sides of the vehicle. Rather than uploading actual images of the lane marks, the marks may be stored in sparse map 800 as a spline or a series of points, thus reducing the size of sparse map 800 and/or the data that must be uploaded remotely by the vehicle.

FIGS. 24A-24D illustrate exemplary point locations that may be detected by vehicle 200 to represent particular lane marks. Similar to the landmarks described above, vehicle 200 may use various image recognition algorithms or software to identify point locations within a captured image. For example, vehicle 200 may recognize a series of edge points, corner points or various other point locations associated with a particular lane mark. FIG. 24A shows a continuous lane mark 2410 that may be detected by vehicle 200. Lane mark 2410 may represent the outside edge of a roadway, represented by a continuous white line. As shown in FIG. 24A, vehicle 200 may be configured to detect a plurality of edge location points 2411 along the lane mark. Location points 2411 may be collected to represent the lane mark at any intervals sufficient to create a mapped lane mark in the sparse map. For example, the lane mark may be represented by one point per meter of the detected edge, one point per every five meters of the detected edge, or at other suitable spacings. In some embodiments, the spacing may be determined by other factors, rather than at set intervals such as, for example, based on points where vehicle 200 has a highest confidence ranking of the location of the detected points. Although FIG. 24A shows edge location points on an interior edge of lane mark 2410, points may be collected on the outside edge of the line or along both edges. Further, while a single line is shown in FIG. 24A, similar edge points may be detected for a double continuous line. For example, points 2411 may be detected along an edge of one or both of the continuous lines.

Vehicle 200 may also represent lane marks differently depending on the type or shape of lane mark. FIG. 24B shows an exemplary dashed lane mark 2420 that may be detected by vehicle 200. Rather than identifying edge points, as in FIG. 24A, vehicle may detect a series of corner points 2421 representing corners of the lane dashes to define the full boundary of the dash. While FIG. 24B shows each corner of a given dash marking being located, vehicle 200 may detect or upload a subset of the points shown in the figure. For example, vehicle 200 may detect the leading edge or leading corner of a given dash mark, or may detect the two corner points nearest the interior of the lane. Further, not every dash mark may be captured, for example, vehicle 200 may capture and/or record points representing a sample of dash marks (e.g., every other, every third, every fifth, etc.) or dash marks at a predefined spacing (e.g., every meter, every five meters, every 10 meters, etc.) Corner points may also be detected for similar lane marks, such as markings showing a lane is for an exit ramp, that a particular lane is ending, or other various lane marks that may have detectable corner points. Corner points may also be detected for lane marks consisting of double dashed lines or a combination of continuous and dashed lines.

In some embodiments, the points uploaded to the server to generate the mapped lane marks may represent other points besides the detected edge points or corner points. FIG. 24C illustrates a series of points that may represent a centerline of a given lane mark. For example, continuous lane 2410 may be represented by centerline points 2441 along a centerline 2440 of the lane mark. In some embodiments, vehicle 200 may be configured to detect these center points using various image recognition techniques, such as convolutional neural networks (CNN), scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) features, or other techniques. Alternatively, vehicle 200 may detect other points, such as edge points 2411 shown in FIG. 24A, and may calculate centerline points 2441, for example, by detecting points along each edge and determining a midpoint between the edge points. Similarly, dashed lane mark 2420 may be represented by centerline points 2451 along a centerline 2450 of the lane mark. The centerline points may be located at the edge of a dash, as shown in FIG. 24C, or at various other locations along the centerline. For example, each dash may be represented by a single point in the geometric center of the dash. The points may also be spaced at a predetermined interval along the centerline (e.g., every meter, 5 meters, 10 meters, etc.). The centerline points 2451 may be detected directly by vehicle 200, or may be calculated based on other detected reference points, such as corner points 2421, as shown in FIG. 24B. A centerline may also be used to represent other lane mark types, such as a double line, using similar techniques as above.

In some embodiments, vehicle 200 may identify points representing other features, such as a vertex between two intersecting lane marks. FIG. 24D shows exemplary points representing an intersection between two lane marks 2460 and 2465. Vehicle 200 may calculate a vertex point 2466 representing an intersection between the two lane marks. For example, one of lane marks 2460 or 2465 may represent a train crossing area or other crossing area in the road segment. While lane marks 2460 and 2465 are shown as crossing each other perpendicularly, various other configurations may be detected. For example, the lane marks 2460 and 2465 may cross at other angles, or one or both of the lane marks may terminate at the vertex point 2466. Similar techniques may also be applied for intersections between dashed or other lane mark types. In addition to vertex point 2466, various other points 2467 may also be detected, providing further information about the orientation of lane marks 2460 and 2465.

Vehicle 200 may associate real-world coordinates with each detected point of the lane mark. For example, location identifiers may be generated, including coordinate for each point, to upload to a server for mapping the lane mark. The location identifiers may further include other identifying information about the points, including whether the point represents a corner point, an edge point, center point, etc. Vehicle 200 may therefore be configured to determine a real-world position of each point based on analysis of the images. For example, vehicle 200 may detect other features in the image, such as the various landmarks described above, to locate the real-world position of the lane marks. This may involve determining the location of the lane marks in the image relative to the detected landmark or determining the position of the vehicle based on the detected landmark and then determining a distance from the vehicle (or target trajectory of the vehicle) to the lane mark. When a landmark is not available, the location of the lane mark points may be determined relative to a position of the vehicle determined based on dead reckoning. The real-world coordinates included in the location identifiers may be represented as absolute coordinates (e.g., latitude/longitude coordinates), or may be relative to other features, such as based on a longitudinal position along a target trajectory and a lateral distance from the target trajectory. The location identifiers may then be uploaded to a server for generation of the mapped lane marks in the navigation model (such as sparse map 800). In some embodiments, the server may construct a spline representing the lane marks of a road segment. Alternatively, vehicle 200 may generate the spline and upload it to the server to be recorded in the navigational model.

FIG. 24E shows an exemplary navigation model or sparse map for a corresponding road segment that includes mapped lane marks. The sparse map may include a target trajectory 2475 for a vehicle to follow along a road segment. As described above, target trajectory 2475 may represent an ideal path for a vehicle to take as it travels the corresponding road segment, or may be located elsewhere on the road (e.g., a centerline of the road, etc.). Target trajectory 2475 may be calculated in the various methods described above, for example, based on an aggregation (e.g., a weighted combination) of two or more reconstructed trajectories of vehicles traversing the same road segment.

In some embodiments, the target trajectory may be generated equally for all vehicle types and for all road, vehicle, and/or environment conditions. In other embodiments, however, various other factors or variables may also be considered in generating the target trajectory. A different target trajectory may be generated for different types of vehicles (e.g., a private car, a light truck, and a full trailer). For example, a target trajectory with relatively tighter turning radii may be generated for a small private car than a larger semi-trailer truck. In some embodiments, road, vehicle and environmental conditions may be considered as well. For example, a different target trajectory may be generated for different road conditions (e.g., wet, snowy, icy, dry, etc.), vehicle conditions (e.g., tire condition or estimated tire condition, brake condition or estimated brake condition, amount of fuel remaining, etc.) or environmental factors (e.g., time of day, visibility, weather, etc.). The target trajectory may also depend on one or more aspects or features of a particular road segment (e.g., speed limit, frequency and size of turns, grade, etc.). In some embodiments, various user settings may also be used to determine the target trajectory, such as a set driving mode (e.g., desired driving aggressiveness, economy mode, etc.).

The sparse map may also include mapped lane marks 2470 and 2480 representing lane marks along the road segment. The mapped lane marks may be represented by a plurality of location identifiers 2471 and 2481. As described above, the location identifiers may include locations in real world coordinates of points associated with a detected lane mark. Similar to the target trajectory in the model, the lane marks may also include elevation data and may be represented as a curve in three-dimensional space. For example, the curve may be a spline connecting three dimensional polynomials of suitable order the curve may be calculated based on the location identifiers. The mapped lane marks may also include other information or metadata about the lane mark, such as an identifier of the type of lane mark (e.g., between two lanes with the same direction of travel, between two lanes of opposite direction of travel, edge of a roadway, etc.) and/or other characteristics of the lane mark (e.g., continuous, dashed, single line, double line, yellow, white, etc.). In some embodiments, the mapped lane marks may be continuously updated within the model, for example, using crowdsourcing techniques. The same vehicle may upload location identifiers during multiple occasions of travelling the same road segment or data may be selected from a plurality of vehicles (such as 1205, 1210, 1215, 1220, and 1225) travelling the road segment at different times. Sparse map 800 may then be updated or refined based on subsequent location identifiers received from the vehicles and stored in the system. As the mapped lane marks are updated and refined, the updated road navigation model and/or sparse map may be distributed to a plurality of autonomous vehicles.

Generating the mapped lane marks in the sparse map may also include detecting and/or mitigating errors based on anomalies in the images or in the actual lane marks themselves. FIG. 24F shows an exemplary anomaly 2495 associated with detecting a lane mark 2490. Anomaly 2495 may appear in the image captured by vehicle 200, for example, from an object obstructing the camera's view of the lane mark, debris on the lens, etc. In some instances, the anomaly may be due to the lane mark itself, which may be damaged or worn away, or partially covered, for example, by dirt, debris, water, snow or other materials on the road. Anomaly 2495 may result in an erroneous point 2491 being detected by vehicle 200. Sparse map 800 may provide the correct the mapped lane mark and exclude the error. In some embodiments, vehicle 200 may detect erroneous point 2491 for example, by detecting anomaly 2495 in the image, or by identifying the error based on detected lane mark points before and after the anomaly. Based on detecting the anomaly, the vehicle may omit point 2491 or may adjust it to be in line with other detected points. In other embodiments, the error may be corrected after the point has been uploaded, for example, by determining the point is outside of an expected threshold based on other points uploaded during the same trip, or based on an aggregation of data from previous trips along the same road segment.

The mapped lane marks in the navigation model and/or sparse map may also be used for navigation by an autonomous vehicle traversing the corresponding roadway. For example, a vehicle navigating along a target trajectory may periodically use the mapped lane marks in the sparse map to align itself with the target trajectory. As mentioned above, between landmarks the vehicle may navigate based on dead reckoning in which the vehicle uses sensors to determine its ego motion and estimate its position relative to the target trajectory. Errors may accumulate over time and vehicle's position determinations relative to the target trajectory may become increasingly less accurate. Accordingly, the vehicle may use lane marks occurring in sparse map 800 (and their known locations) to reduce the dead reckoning-induced errors in position determination. In this way, the identified lane marks included in sparse map 800 may serve as navigational anchors from which an accurate position of the vehicle relative to a target trajectory may be determined.

FIG. 25A shows an exemplary image 2500 of a vehicle's surrounding environment that may be used for navigation based on the mapped lane marks. Image 2500 may be captured, for example, by vehicle 200 through image capture devices 122 and 124 included in image acquisition unit 120. Image 2500 may include an image of at least one lane mark 2510, as shown in FIG. 25A. Image 2500 may also include one or more landmarks 2521, such as road sign, used for navigation as described above. Some elements shown in FIG. 25A, such as elements 2511, 2530, and 2520 which do not appear in the captured image 2500 but are detected and/or determined by vehicle 200 are also shown for reference.

Using the various techniques described above with respect to FIGS. 24A-D and 24F, a vehicle may analyze image 2500 to identify lane mark 2510. Various points 2511 may be detected corresponding to features of the lane mark in the image. Points 2511, for example, may correspond to an edge of the lane mark, a corner of the lane mark, a midpoint of the lane mark, a vertex between two intersecting lane marks, or various other features or locations. Points 2511 may be detected to correspond to a location of points stored in a navigation model received from a server. For example, if a sparse map is received containing points that represent a centerline of a mapped lane mark, points 2511 may also be detected based on a centerline of lane mark 2510.

The vehicle may also determine a longitudinal position represented by element 2520 and located along a target trajectory. Longitudinal position 2520 may be determined from image 2500, for example, by detecting landmark 2521 within image 2500 and comparing a measured location to a known landmark location stored in the road model or sparse map 800. The location of the vehicle along a target trajectory may then be determined based on the distance to the landmark and the landmark's known location. The longitudinal position 2520 may also be determined from images other than those used to determine the position of a lane mark. For example, longitudinal position 2520 may be determined by detecting landmarks in images from other cameras within image acquisition unit 120 taken simultaneously or near simultaneously to image 2500. In some instances, the vehicle may not be near any landmarks or other reference points for determining longitudinal position 2520. In such instances, the vehicle may be navigating based on dead reckoning and thus may use sensors to determine its ego motion and estimate a longitudinal position 2520 relative to the target trajectory. The vehicle may also determine a distance 2530 representing the actual distance between the vehicle and lane mark 2510 observed in the captured image(s). The camera angle, the speed of the vehicle, the width of the vehicle, or various other factors may be accounted for in determining distance 2530.

FIG. 25B illustrates a lateral localization correction of the vehicle based on the mapped lane marks in a road navigation model. As described above, vehicle 200 may determine a distance 2530 between vehicle 200 and a lane mark 2510 using one or more images captured by vehicle 200. Vehicle 200 may also have access to a road navigation model, such as sparse map 800, which may include a mapped lane mark 2550 and a target trajectory 2555. Mapped lane mark 2550 may be modeled using the techniques described above, for example using crowdsourced location identifiers captured by a plurality of vehicles. Target trajectory 2555 may also be generated using the various techniques described previously. Vehicle 200 may also determine or estimate a longitudinal position 2520 along target trajectory 2555 as described above with respect to FIG. 25A. Vehicle 200 may then determine an expected distance 2540 based on a lateral distance between target trajectory 2555 and mapped lane mark 2550 corresponding to longitudinal position 2520. The lateral localization of vehicle 200 may be corrected or adjusted by comparing the actual distance 2530, measured using the captured image(s), with the expected distance 2540 from the model.

FIGS. 25C and 25D provide illustrations associated with another example for localizing a host vehicle during navigation based on mapped landmarks/objects/features in a sparse map. FIG. 25C conceptually represents a series of images captured from a vehicle navigating along a road segment 2560. In this example, road segment 2560 includes a straight section of a two-lane divided highway delineated by road edges 2561 and 2562 and center lane marking 2563. As shown, the host vehicle is navigating along a lane 2564, which is associated with a mapped target trajectory 2565. Thus, in an ideal situation (and without influencers such as the presence of target vehicles or objects in the roadway, etc.) the host vehicle should closely track the mapped target trajectory 2565 as it navigates along lane 2564 of road segment 2560. In reality, the host vehicle may experience drift as it navigates along mapped target trajectory 2565. For effective and safe navigation, this drift should be maintained within acceptable limits (e.g., +/−10 cm of lateral displacement from target trajectory 2565 or any other suitable threshold). To periodically account for drift and to make any needed course corrections to ensure that the host vehicle follows target trajectory 2565, the disclosed navigation systems may be able to localize the host vehicle along the target trajectory 2565 (e.g., determine a lateral and longitudinal position of the host vehicle relative to the target trajectory 2565) using one or more mapped features/objects included in the sparse map.

As a simple example, FIG. 25C shows a speed limit sign 2566 as it may appear in five different, sequentially captured images as the host vehicle navigates along road segment 2560. For example, at a first time, t₀, sign 2566 may appear in a captured image near the horizon. As the host vehicle approaches sign 2566, in subsequentially captured images at times t₁, t₂, t₃, and t₄, sign 2566 will appear at different 2D X-Y pixel locations of the captured images. For example, in the captured image space, sign 2566 will move downward and to the right along curve 2567 (e.g., a curve extending through the center of the sign in each of the five captured image frames). Sign 2566 will also appear to increase in size as it is approached by the host vehicle (i.e., it will occupy a great number of pixels in subsequently captured images).

These changes in the image space representations of an object, such as sign 2566, may be exploited to determine a localized position of the host vehicle along a target trajectory. For example, as described in the present disclosure, any detectable object or feature, such as a semantic feature like sign 2566 or a detectable non-semantic feature, may be identified by one or more harvesting vehicles that previously traversed a road segment (e.g., road segment 2560). A mapping server may collect the harvested drive information from a plurality of vehicles, aggregate and correlate that information, and generate a sparse map including, for example, a target trajectory 2565 for lane 2564 of road segment 2560. The sparse map may also store a location of sign 2566 (along with type information, etc.). During navigation (e.g., prior to entering road segment 2560), a host vehicle may be supplied with a map tile including a sparse map for road segment 2560. To navigate in lane 2564 of road segment 2560, the host vehicle may follow mapped target trajectory 2565.

The mapped representation of sign 2566 may be used by the host vehicle to localize itself relative to the target trajectory. For example, a camera on the host vehicle will capture an image 2570 of the environment of the host vehicle, and that captured image 2570 may include an image representation of sign 2566 having a certain size and a certain X-Y image location, as shown in FIG. 25D. This size and X-Y image location can be used to determine the host vehicle's position relative to target trajectory 2565. For example, based on the sparse map including a representation of sign 2566, a navigation processor of the host vehicle can determine that in response to the host vehicle traveling along target trajectory 2565, a representation of sign 2566 should appear in captured images such that a center of sign 2566 will move (in image space) along line 2567. If a captured image, such as image 2570, shows the center (or other reference point) displaced from line 2567 (e.g., the expected image space trajectory), then the host vehicle navigation system can determine that at the time of the captured image it was not located on target trajectory 2565. From the image, however, the navigation processor can determine an appropriate navigational correction to return the host vehicle to the target trajectory 2565. For example, if analysis shows an image location of sign 2566 that is displaced in the image by a distance 2572 to the left of the expected image space location on line 2567, then the navigation processor may cause a heading change by the host vehicle (e.g., change the steering angle of the wheels) to move the host vehicle leftward by a distance 2573. In this way, each captured image can be used as part of a feedback loop process such that a difference between an observed image position of sign 2566 and expected image trajectory 2567 may be minimized to ensure that the host vehicle continues along target trajectory 2565 with little to no deviation. Of course, the more mapped objects that are available, the more often the described localization technique may be employed, which can reduce or eliminate drift-induced deviations from target trajectory 2565.

The process described above may be useful for detecting a lateral orientation or displacement of the host vehicle relative to a target trajectory. Localization of the host vehicle relative to target trajectory 2565 may also include a determination of a longitudinal location of the target vehicle along the target trajectory. For example, captured image 2570 includes a representation of sign 2566 as having a certain image size (e.g., 2D X-Y pixel area). This size can be compared to an expected image size of mapped sign 2566 as it travels through image space along line 2567 (e.g., as the size of the sign progressively increases, as shown in FIG. 25C). Based on the image size of sign 2566 in image 2570, and based on the expected size progression in image space relative to mapped target trajectory 2565, the host vehicle can determine its longitudinal position (at the time when image 2570 was captured) relative to target trajectory 2565. This longitudinal position coupled with any lateral displacement relative to target trajectory 2565, as described above, allows for full localization of the host vehicle relative to target trajectory 2565, as the host vehicle navigates along road 2560.

FIGS. 25C and 25D provide just one example of the disclosed localization technique using a single mapped object and a single target trajectory. In other examples, there may be many more target trajectories (e.g., one target trajectory for each viable lane of a multi-lane highway, urban street, complex junction, etc.) and there may be many more mapped available for localization. For example, a sparse map representative of an urban environment may include many objects per meter available for localization.

FIG. 26A is a flowchart showing an exemplary process 2600A for mapping a lane mark for use in autonomous vehicle navigation, consistent with disclosed embodiments. At step 2610, process 2600A may include receiving two or more location identifiers associated with a detected lane mark. For example, step 2610 may be performed by server 1230 or one or more processors associated with the server. The location identifiers may include locations in real-world coordinates of points associated with the detected lane mark, as described above with respect to FIG. 24E. In some embodiments, the location identifiers may also contain other data, such as additional information about the road segment or the lane mark. Additional data may also be received during step 2610, such as accelerometer data, speed data, landmarks data, road geometry or profile data, vehicle positioning data, ego motion data, or various other forms of data described above. The location identifiers may be generated by a vehicle, such as vehicles 1205, 1210, 1215, 1220, and 1225, based on images captured by the vehicle. For example, the identifiers may be determined based on acquisition, from a camera associated with a host vehicle, of at least one image representative of an environment of the host vehicle, analysis of the at least one image to detect the lane mark in the environment of the host vehicle, and analysis of the at least one image to determine a position of the detected lane mark relative to a location associated with the host vehicle. As described above, the lane mark may include a variety of different marking types, and the location identifiers may correspond to a variety of points relative to the lane mark. For example, where the detected lane mark is part of a dashed line marking a lane boundary, the points may correspond to detected corners of the lane mark. Where the detected lane mark is part of a continuous line marking a lane boundary, the points may correspond to a detected edge of the lane mark, with various spacings as described above. In some embodiments, the points may correspond to the centerline of the detected lane mark, as shown in FIG. 24C, or may correspond to a vertex between two intersecting lane marks and at least one two other points associated with the intersecting lane marks, as shown in FIG. 24D.

At step 2612, process 2600A may include associating the detected lane mark with a corresponding road segment. For example, server 1230 may analyze the real-world coordinates, or other information received during step 2610, and compare the coordinates or other information to location information stored in an autonomous vehicle road navigation model. Server 1230 may determine a road segment in the model that corresponds to the real-world road segment where the lane mark was detected.

At step 2614, process 2600A may include updating an autonomous vehicle road navigation model relative to the corresponding road segment based on the two or more location identifiers associated with the detected lane mark. For example, the autonomous road navigation model may be sparse map 800, and server 1230 may update the sparse map to include or adjust a mapped lane mark in the model. Server 1230 may update the model based on the various methods or processes described above with respect to FIG. 24E. In some embodiments, updating the autonomous vehicle road navigation model may include storing one or more indicators of position in real world coordinates of the detected lane mark. The autonomous vehicle road navigation model may also include a at least one target trajectory for a vehicle to follow along the corresponding road segment, as shown in FIG. 24E.

At step 2616, process 2600A may include distributing the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. For example, server 1230 may distribute the updated autonomous vehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and 1225, which may use the model for navigation. The autonomous vehicle road navigation model may be distributed via one or more networks (e.g., over a cellular network and/or the Internet, etc.), through wireless communication paths 1235, as shown in FIG. 12 .

In some embodiments, the lane marks may be mapped using data received from a plurality of vehicles, such as through a crowdsourcing technique, as described above with respect to FIG. 24E. For example, process 2600A may include receiving a first communication from a first host vehicle, including location identifiers associated with a detected lane mark, and receiving a second communication from a second host vehicle, including additional location identifiers associated with the detected lane mark. For example, the second communication may be received from a subsequent vehicle travelling on the same road segment, or from the same vehicle on a subsequent trip along the same road segment. Process 2600A may further include refining a determination of at least one position associated with the detected lane mark based on the location identifiers received in the first communication and based on the additional location identifiers received in the second communication. This may include using an average of the multiple location identifiers and/or filtering out “ghost” identifiers that may not reflect the real-world position of the lane mark.

FIG. 26B is a flowchart showing an exemplary process 2600B for autonomously navigating a host vehicle along a road segment using mapped lane marks. Process 2600B may be performed, for example, by processing unit 110 of autonomous vehicle 200. At step 2620, process 2600B may include receiving from a server-based system an autonomous vehicle road navigation model. In some embodiments, the autonomous vehicle road navigation model may include a target trajectory for the host vehicle along the road segment and location identifiers associated with one or more lane marks associated with the road segment. For example, vehicle 200 may receive sparse map 800 or another road navigation model developed using process 2600A. In some embodiments, the target trajectory may be represented as a three-dimensional spline, for example, as shown in FIG. 9B. As described above with respect to FIGS. 24A-F, the location identifiers may include locations in real world coordinates of points associated with the lane mark (e.g., corner points of a dashed lane mark, edge points of a continuous lane mark, a vertex between two intersecting lane marks and other points associated with the intersecting lane marks, a centerline associated with the lane mark, etc.).

At step 2621, process 2600B may include receiving at least one image representative of an environment of the vehicle. The image may be received from an image capture device of the vehicle, such as through image capture devices 122 and 124 included in image acquisition unit 120. The image may include an image of one or more lane marks, similar to image 2500 described above.

At step 2622, process 2600B may include determining a longitudinal position of the host vehicle along the target trajectory. As described above with respect to FIG. 25A, this may be based on other information in the captured image (e.g., landmarks, etc.) or by dead reckoning of the vehicle between detected landmarks.

At step 2623, process 2600B may include determining an expected lateral distance to the lane mark based on the determined longitudinal position of the host vehicle along the target trajectory and based on the two or more location identifiers associated with the at least one lane mark. For example, vehicle 200 may use sparse map 800 to determine an expected lateral distance to the lane mark. As shown in FIG. 25B, longitudinal position 2520 along a target trajectory 2555 may be determined in step 2622. Using spare map 800, vehicle 200 may determine an expected distance 2540 to mapped lane mark 2550 corresponding to longitudinal position 2520.

At step 2624, process 2600B may include analyzing the at least one image to identify the at least one lane mark. Vehicle 200, for example, may use various image recognition techniques or algorithms to identify the lane mark within the image, as described above. For example, lane mark 2510 may be detected through image analysis of image 2500, as shown in FIG. 25A.

At step 2625, process 2600B may include determining an actual lateral distance to the at least one lane mark based on analysis of the at least one image. For example, the vehicle may determine a distance 2530, as shown in FIG. 25A, representing the actual distance between the vehicle and lane mark 2510. The camera angle, the speed of the vehicle, the width of the vehicle, the position of the camera relative to the vehicle, or various other factors may be accounted for in determining distance 2530.

At step 2626, process 2600B may include determining an autonomous steering action for the host vehicle based on a difference between the expected lateral distance to the at least one lane mark and the determined actual lateral distance to the at least one lane mark. For example, as described above with respect to FIG. 25B, vehicle 200 may compare actual distance 2530 with an expected distance 2540. The difference between the actual and expected distance may indicate an error (and its magnitude) between the vehicle's actual position and the target trajectory to be followed by the vehicle. Accordingly, the vehicle may determine an autonomous steering action or other autonomous action based on the difference. For example, if actual distance 2530 is less than expected distance 2540, as shown in FIG. 25B, the vehicle may determine an autonomous steering action to direct the vehicle left, away from lane mark 2510. Thus, the vehicle's position relative to the target trajectory may be corrected. Process 2600B may be used, for example, to improve navigation of the vehicle between landmarks.

Processes 2600A and 2600B provide examples only of techniques that may be used for navigating a host vehicle using the disclosed sparse maps. In other examples, processes consistent with those described relative to FIGS. 25C and 25D may also be employed.

Crowd-Sourced 3D Points and Point Cloud Alignment

As described above, a road navigation model (such as a sparse map) may be generated based on data collected by a plurality of vehicles traversing a road segment. For example, each vehicle may capture images of landmarks, such as lane marks, road signs, traffic lights, light posts, potholes, trees, buildings, or other features that may be present along a roadway. These features may be recognized by a navigation system of the vehicle and may be transmitted to a remote server. The server may then combine data from multiple drives along the road segment (e.g., by the same vehicle or different vehicles) and may align the drive information to generate the sparse map, as described in greater detail above.

When aligning information from vehicles moving in the same direction of travel along the road segment, two-dimensional (2D) points may be used to generate the sparse map. As used herein, a 2D point may include a location of an identified feature represented in image coordinates (e.g., x and y pixel locations within the image). These 2D points may be defined for various features identified within the images. In some embodiments, additional information, such as a feature type classifier may also be used to define the 2D points, as described in greater detail below. The 2D points (and any associated type classifications) along with location information for the host vehicle (e.g., GPS location data) may be used to align drives in the same direction. However, these 2D points may not be sufficient for aligning multiple drives in different directions because the same road segment may look different when viewed from the same direction. For example, the same road sign when viewed from one direction may look completely different from the opposite direction. Therefore, it may be difficult for a system to correlate points representing the road sign from one direction, with points representing the sign collected from the other direction. Accordingly, in order to fully-align drive data from opposing directions of travel, some form of “link” may be needed to correlate the collected points.

To accurately align the opposing drive information, three-dimensional (3D) points may be collected by host vehicles. For example, the 3D points may be based on the image coordinates included in the 2D points described above, as well range or depth data indicating a distance from the host vehicle to the road feature. Based on this additional depth information, data representing an object viewed from one direction may be correlated with data representing the same object viewed from the opposite direction. In particular, a cloud of 3D points captured from a first direction may be compared to a cloud of 3D points captured from an opposing direction. The disclosed systems may match 3D points from the two sets along a segment of a roadway and determine a transformation that best aligns the two sets of points. Once detected features in images captured in opposing drive directions have been correlated to each other, the drive information from the different vehicles may be aligned without having to reapply the transformation. In some embodiments, the collection and transmission of 3D points may place a high computational demand on vehicles. Accordingly, the disclosed systems and methods may include crowd-sourcing 3D points from multiple vehicles and aggregating the collected data into a dense 3D point cloud.

FIG. 27 illustrates an example image that may be captured by a host vehicle for aligning drive information, consistent with the disclosed embodiments. Image 2700 may be captured by a camera of the host vehicle, such as image capture devices 122, 124, and/or 126. In the example shown in FIG. 27 , the image may be captured from a front-facing camera of the host vehicle as the vehicle travels along a road segment.

The host vehicle may be configured to recognize features and objects within the image. For example, image 2700 may include a road sign 2710 and a pothole 2720, among other objects and/or road features. In some embodiments, the host vehicle may identify features having a recognized or standardized classification or type. These features, which may be referred to as “semantic” features, may include road signs, (e.g., speed limit signs, warning signs, directional signs, etc.), potholes, trees, billboards, buildings, or any other detectable features that can be recognized and classified. For semantic features, the host vehicle may define the position of the feature based on one or more 2D points as well as an object type classifier. This type classifier may allow the system to determine a size associated with the semantic object without collecting additional points of the semantic object in order to represent the size. For example, a 30 mph speed limit sign may be represented by a single center point (or any other representative point location) along with a type value representing the 30 mph sign. From the type value, a server system may know that the 30 mph sign is 2 feet by 3.5 feet in size. Other types of descriptors may also be collected or determined for detected semantic features. For example, this may include a bounding box for the semantic feature object, or the like.

In some embodiments, the host vehicle may detect other points in an image that do not have a predefined type classification, but that may be repeatedly identified or recognized based on detecting a certain point. These features, which may be referred to as “non-semantic” features, may include, a tip of a pole, a corner of a building, a base of a post, or other unique points. Using an image analysis algorithm, the host vehicle may generate a unique key based on pixel data surrounding the recognized non-semantic feature point in the image. Other vehicles applying the algorithm to pixel data surrounding representations of the non-semantic feature may generate the same unique key. Accordingly, the non-semantic feature may be recognized across multiple drives to allow alignment of the drive data, similar to semantic features.

In some embodiments, these semantic and non-semantic features may be identified based on 2D points recognized in an image. For example, a navigation system for the host vehicle may be configured to identify a 2D point 2712 associated with road sign 2710. This may include performing various edge detection or other image processing techniques to identify road sign 2710 as described throughout the present disclosure. 2D point 2712 may be represented using a coordinate system of image 2700. For example, 2D point 2712 may be represented based on x and y coordinates relative to image 2700, as shown in FIG. 27 . While 2D point 2712 is shown in FIG. 27 using image coordinates based on a top left corner of image 2700, various other 2D coordinate systems may be used (e.g., having an origin in other locations of the image). 2D point 2712 may further be associated with an object type classification indicating a type for road sign 2710. For example, the object type classification may be a value indicating road sign 2710 is a road sign, a speed limit sign, a 30 MPH speed limit sign, or various other classifiers. The object type classification may be a numerical code, an alphanumerical code, a semantic description, or any other suitable value. 2D points for non-semantic features may be detected as well. For example, a 2D point 2722 may be defined for a corner of pothole 2720.

As described above, the 2D image coordinates may be used to align drive information captured by vehicles traveling in the same direction along a road segment. For example, because the vehicles generally follow the same path, road sign 2710 will have the same general appearance within different captured images. Accordingly, 2D point 2712 along with GPS data or other information indicating a location of the host vehicle when image 2700 was captured may be used to align drive data from the same direction of travel. For drive data collected from the opposite direction of travel, the back of road sign 2710 may be visible but may not appear the same as the front of road sign 2710. Thus, a 2D point representing road sign 2710 captured from the opposite direction of travel may not easily be correlated with 2D point 2712. Accordingly, to generate a cohesive sparse map including drive data from drives in opposite directions, 3D points may be collected by the host vehicles and transmitted to a server. These 3D points may be generated for objects represented in images captured by the host vehicles. In some embodiments, the 3D points may be based on the same objects associated with semantic and non-semantic features identified in the image, as described above.

FIG. 28 illustrates an example 3D point 2812 that may be obtained by a host vehicle, consistent with the disclosed embodiments. Image 2700 described above may be captured by a front-facing camera 2822 of a host vehicle 2820. For example, host vehicle 2820 may be equipped with an image capture device or camera, such as image capture devices 122, 124, and 126, as described in greater detail above, which may correspond to camera 2822. Host vehicle 2820 may be equipped with a location sensor 2824 configured to determine a location of host vehicle 2820. In some embodiments, location sensor 2824 may be a GPS receiver (which may correspond to position sensor 130 described above). Location sensor 2824 may include other forms of sensors, including accelerometers, speed sensors, compasses, or other sensors that may help track the motion of host vehicle 2820. For example, a location of host vehicle 2820 may be determined, at least in part, based on a determined ego motion of the vehicle between detected locations. Additional details regarding ego motion estimation are provided above. Host vehicle 2820 may further be equipped with a processor, such as processing device 110, described above. The processor of host vehicle 2820 may be configured to analyze images captured by cameras mounted on host vehicle 2710 to determine 2D and/or 3D point locations.

Host vehicle may be configured to determine a 3D location 2812 associated with road sign 2710, as shown in FIG. 28 . As described above, 3D location 3812 may include depth information indicating a distance from host vehicle 2820 to road sign 2710. For example, the navigation system of host vehicle 2820 may determine a depth d representing a distance to 3D point 2812. Depth d may be determined or estimated in various ways. In some embodiments, this may include structure from motion (SfM) techniques based on a plurality of images captured using camera 2822. For example, camera 2822 may capture first and second images showing road sign 2710 at different times. This may include analyzing consecutively captured images, images captured at particular time intervals, or the like. The system may determine a change in position of road sign 2710 within the images, which may be correlated with a change in position of camera 2822 (e.g., determined using location sensor 2824). Based on this correlation, a three-dimensional location of road sign 2710 may be estimated, resulting in depth d. In some embodiments, this may include applying a matching algorithm such as a Lukas-Kanade tracker algorithm to correlate features between images and generate 3D points. In some embodiments, a trained machine learning model may be used to determine depth d. For example, a training set of images or groups of images along with depth data may be input into a machine learning model. The trained machine learning model may be configured to determine depth d based on two or more images including road sign 2710 captured at different positions.

In some embodiments, the change in position of camera 2822 between the images may be based on GPS data associated with each of the images. For example, position sensor 2824 may determine a GPS location when a first image is captured and a GPS location when a second image is captured and the distance between these locations may be used for determining 3D point 2812. In some embodiments, ego motion of host vehicle 2820 may be used in place of or in addition to GPS locations. For example, a GPS location may be determined when the first image is captured. The system may then track an ego motion of host vehicle 2824 from the location where the first image is captured to the location where the next image is captured. Accordingly, the location of the second image may be determined based on the ego motion alone, or in combination with additional GPS information. Due to the relatively low accuracy of GPS positioning (e.g., 10 m accuracy, 5 m accuracy, etc.) the use of ego motion may help refine actual camera positions and improve the accuracy of depth d.

While depth d may be determined using structure from motion techniques, as described above, various other techniques may be used. In some embodiments, depth d may be determined based on data from sensors other than camera 2822. For example, host vehicle 2820 may include LIDAR sensors, LED proximity sensors, ultrasonic sensors, laser rangefinders or other sensors that may indicate depth d. This process for determining 3D points may be applied to various other objects or features within the environment of host vehicle 2820. For example, the same or similar processes as those described for FIG. 28 may be performed for pothole 2720. As noted above, more points may be used for pothole 2720 since pothole 2720 may not have a shape, size, or other characteristics that are uniform with other potholes. Accordingly, additional points may be used to more precisely define the shape or location of pothole 2720.

In some embodiments, 3D point 2812 may be represented based on a three-dimensional real-world coordinates. For example, a coordinate system may be defined based on host vehicle 2820, and 3D point 2812 may be represented as X, Y, and Z coordinates, as shown in FIG. 28 . Various other coordinate systems may be used, such as a coordinate system based on a sparse map, a coordinate system based on a particular road segment, a global coordinate system (e.g., latitude/longitude/elevation), or any other data defining a real-world position. The X, Y, and Z coordinates may be determined using the structure from motion techniques described above, based on LIDAR or other sensors, or various other techniques.

As a result, a group of 3D points may be determined and collected for features along a road segment. These 3D points may be transmitted to a server and used to generate a road navigation model, such as a sparse map. FIG. 29 illustrates an example system 2900 for generating a sparse map based on 3D point clouds, consistent with the disclosed embodiments. System 2900 may include a server 2910 configured to receive information from one or more host vehicles and generate sparse maps based on the received data. For example, server 2910 may be configured to receive data from vehicles 2920, 2930, and 2940, as shown in FIG. 29 . In particular, server 2910 may collect data from vehicles 2920, 2930, and 2940 travelling on a road segment at different times and may process the collected data to generate sparse map or update an existing sparse map. Server 2910 may also transmit the sparse map or update data for the sparse map to one or more autonomous or semi-autonomous vehicles, which may be used for navigation. In some embodiments, server 2910 may correspond to sever 1230, as described above. Accordingly, any of the descriptions or disclosures made herein in reference to server 1230 may also apply to server 2910, and vice versa.

Server 2910 may receive a collection of 3D points 2922, 2924, and 2926 captured by host vehicle 2920. For example, host vehicle 2920 may correspond to host vehicle 2820, and may identify 3D points 2922, 2924, and 2926 associated with various features along a road segment, as described above with respect to FIG. 28 . In some embodiments, server 2910 may receive additional 3D points captured by vehicles traveling in the same direction along a road segment as host vehicle 2920. For example, a second host vehicle 2940 may traverse the road segment in the same direction as host vehicle 2920 and may collect 3D points corresponding to the same features as one or more of 3D points 2922, 2924, and 2926. Server 2910 may combine data collected from host vehicles 2920 and 2940 (as well as other vehicles traversing the road segment in the same direction) and align the data, as described in further detail above. In some embodiments, host vehicles 2920 and 2940 may collect both 2D and 3D points. The 2D points may be associated with the same features as 3D points 2922, 2924, and 2926 (e.g., as a 2D portion of the data representing these points), or may be separate points. In some embodiments, server 2910 may align the data acquired from host vehicles 2920 and 2940 using the 2D point data, as described throughout the present disclosure. It is to be understood, however, that similar alignment may be performed using 3D points alone or in combination with the 2D points.

Server 2910 may further receive a group of 3D points determined by host vehicles traveling along the road segment in an opposite direction. For example, server 2910 may receive 3D points 2932, 2934, and 2936 captured by host vehicle 2930, as shown in FIG. 29 . 3D points may be collected by host vehicle 2930 as described above with respect to FIG. 28 . One or more of 3D points 2932, 2934, and 2936 may correspond to the same features as 3D points 2922, 2924, and 2926. For example, 3D point 2922 and 3D point 2932 may correspond to the same object along a road segment, such as a road sign, a light pole, a pothole, a lane mark, a road edge, or the like. As with the 3D points captured by host vehicles 2920 and 2940, server 2910 may collect 3D point data from multiple drives along the same direction of travel as host vehicle 2930. Accordingly, sever 2910 may be configured to align “crowd-sourced” sets of 3D points from different drive directions to generate the sparse map. Server may then align the set of 3D points 2922, 2924, and 2926 (or crowd-sourced points associated with 3D points 2922, 2924, and 2926) with the set of 3D points 2932, 2934, and 2936 (or crowd-sourced points associated with 3D points 2932, 2934, and 2936) to align drive data captured from opposing directions of travel. For example, server 2910 may determine that 3D points 2922 and 2932 (and other sets of points) are associated with the same road feature and may determine a transformation to align the two sets of 3D points.

FIG. 30 illustrates an example alignment process that may be performed for points captured from different drive directions, consistent with the disclosed embodiments. Server 2910 may receive unaligned data 3010, which may include a plurality of points 3012 collected by vehicles in a first direction, and a plurality of points 3014 collected by vehicles traveling in a second direction along the same road segment. Server 2910 may perform a transformation 3030 on one or both of the sets of 3D points to generate aligned data 3020. The transformation may include translating or rotating one or both of the sets of points from opposing direction to find a closest fit between the sets of points. For example, this may include an iterative process of translating and/or rotating a set of points to minimize the difference between the point clouds, such as an iterative closest point (ICP) algorithm. As shown in aligned data 3020, points 3012 and 3014 may be more closely aligned based on transformation 3030. In some embodiments, not all points may correspond directly to points captured from the other direction of travel. For example, point 3022 may not have an equivalent point captured by vehicles in the opposing direction of travel. This may be due to an edge or feature not being visible from the opposing direction, due to variations in how points are defined, or other scenarios. Transformation 3030 may include point 3022 in the aligned data, as shown in FIG. 30 , or may perform other actions, such as omitting point 3022, flagging point 3022, or the like.

In some embodiments, once transformation 3030 has been performed, future drive data may be aligned without performing transformation 3030 as the correlations between points may already be defined. For example, referring to FIG. 29 , server 2910 may have previously established a correlation between points 2922 and 2932 through transformation 3030, as described above. Accordingly, in later drives, when vehicle 2930 (or other vehicles traveling in the same direction as vehicle 2930) identify point 2932, server 2910 may associate it with point 2922. Accordingly, the drive data captured along the two directions of travel may be aligned without running an iterative closest point algorithm or other form of transformation process. The drive data may be aligned on a segment-by-segment basis along a roadway.

In some embodiments, crowd-sourcing of the 3D points collected in the same direction of travel may improve the alignment of drive data from opposing directions. In particular, more 3D points may be needed to provide a statistically significant correlation than are needed for aligning drives based on 2D points. For example, the 3D points may not be as precise as the 2D point data due to potential errors in determining depth d. As another example, more points may be used to define object corners, edges, or other features using 3D points than with 2D points, where single points may be used to represent road signs or other commonly recognized objects. In some embodiments, a set of 3D points on the order of 10× more than a number of 2D points (or greater) may be needed to accurately align drive data in the sparse map. Accordingly, a relatively large number of 3D points may be used to obtain more accurate alignments.

In some embodiments, it may not be feasible to capture the number of 3D points for aligning a single drive along a particular direction of travel. For example, the data associated with the 3D points may exceed the processing capabilities of a processor of a single host vehicle, the storage capabilities of the host vehicle, bandwidth limits for transmitting data, or other limitation. Accordingly, server 2910 may crowd source 3D point data from multiple drives in the same direction. As an illustrative example, if 200 3D points per meter of roadway are to be collected, and each host vehicle may process and transmit up to 40 3D points per meter, then server 2910 may capture 3D points from five drives (e.g., from five different host vehicles or from multiple drives by the same host vehicle or vehicles) to acquire the requisite data set. If more vehicles are available for data collection, server 2910 may acquire one point per meter from 200 vehicles (or 2 points per meter from 100 vehicles, etc.). In some embodiments, the number of points provided by each vehicle may be configurable through settings of server 2910 or individual host vehicles. For example, server 2910 may recognize a region where alignment between different drive directions is needed, and may define a number of points per meter to be collected by host vehicles in the region. For example, this may include transmitting a request or other information defining the requirement for 3D points. In some embodiments, the set point for data collection may be based on a number of vehicles traversing the road segment. For example, if the road segment is frequently travelled, server 2910 may reduce the number of points collected per vehicle since more vehicles are available than in rural or less traveled regions. In some embodiments, the host vehicles may define a limit for data collection. For example, if a host vehicle is processing or transmitting other data with a higher priority, the host vehicle may define a maximum number of 3D points per meter it is capable of transmitting. Accordingly, the load may be distributed to other vehicles by server 2910.

FIG. 31 is a flowchart showing an example process 3100 for harvesting data for a sparse map, consistent with the disclosed embodiments. Process 3100 may be performed by at least one processing device of a host vehicle, such as processing unit 110, as described above. It is to be understood that throughout the present disclosure, the term “processor” is used as a shorthand for “at least one processor.” In other words, a processor may include one or more structures that perform logic operations whether such structures are collocated, connected, or disbursed. In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 3100. Further, process 3100 is not necessarily limited to the steps shown in FIG. 31 , and any steps or processes of the various embodiments described throughout the present disclosure may also be included in process 3100, including those described above with respect to FIGS. 27-30 .

In step 3110, process 3100 may include receiving a plurality of images captured by a camera onboard a host vehicle as the host vehicle travels along a road segment in a first direction. The plurality of images may be representative of an environment of the host vehicle. For example, step 3110 may include acquiring image 2700 and similar images as host vehicle 2820 traverses a road segment. Accordingly, the plurality of images may be captured by image capture devices 122, 124, and/or 126, as described above.

In step 3120, process 3100 may include detecting one or more semantic features represented in one or more of the plurality of images. For example, this may include detecting semantic features associated with road sign 2710 and/or pothole 2720. Various other types of objects may be detected, such as lamp posts, lane marks, road edges, trees, barriers, traffic lights, buildings, or any other objects that may appear in images captured along a roadway. The one or more semantic features may each be associated with a predetermined object type classification. As described above, the object type classification may be any value identifying a type of object associated with the detected semantic feature.

In step 3130, process 3100 may include identifying at least one position descriptor associated with each of the detected one or more semantic features. As used herein, a position descriptor may include any information at least partially defining a location of a feature. In some embodiments, the at least one position descriptor associated with each of the detected one or more semantic features may include a two-dimensional image position. For example, if road sign 2710 is detected in step 3120, step 3130 may include identifying 2D point 2712, as described above with respect to FIG. 27 . The two-dimensional image position may be represented based on a coordinate system associated with an image. For example, the at least one two-dimensional image position may include an x-y position relative to at least one of the plurality of images.

In some embodiments, the at least one position descriptor associated with each of the detected one or more semantic features may include a three-dimensional point location. For example, the position descriptor may include 3D point 2812. Accordingly, the three-dimensional point location may be determined based on analysis of representations of the one or more detected objects across two or more of the plurality of images and based on an output of at least one ego motion sensor associated with the host vehicle, as described above. For example, the three-dimensional point location may be determined based on an output of position sensor 2824. The ego motion sensor may include at least one of a speedometer, an accelerometer, or a GPS receiver.

In step 3140, process 3100 may include identifying three-dimensional feature points associated one or more detected objects represented in at least one of the plurality of images. In some embodiments, a number of identified position descriptors associated with detected semantic features may be greater than a number of identified three-dimensional points. In some embodiments, at least some of the one or more detected objects may be included within the one or more detected semantic features. Similar to the position descriptors, the three-dimensional points may correspond to portions of objects in the image. For example, the three-dimensional feature points may be associated with one or more of edges or corners of at least one surface associated the at least some of the one or more detected objects. In some embodiments, each of the three-dimensional feature points may include an indicator of depth relative to the camera. For example, each of the three-dimensional feature points may include an x-y position relative to at least one of the plurality of images along with an indicator of range relative to the camera. For example, step 3140 may include identifying 3D point 2812 as described above. Accordingly, step 3140 may include determining a depth d, as shown in FIG. 28 . The indicator of range may be determined in various ways, as described above. In some embodiments, each of the three-dimensional feature points includes an X-Y-Z location relative to a real-world origin, as shown in FIG. 28 . In some embodiments, each of the three-dimensional feature points may be determined based on analysis of representations of the one or more detected objects across two or more of the plurality of images and based on an output of at least one ego motion sensor associated with the host vehicle, as described above. The ego motion sensor may include at least one of a speedometer, an accelerometer, or a GPS receiver. For example, the ego motion sensor may correspond to position sensor 2824.

In step 3150, process 3100 may include receiving position information for each of the plurality of images. The position information may be indicative of a position of the camera when each of the plurality of images was captured. For example, the position information may be received from position sensor 2824. In some embodiments, the position information may include at least one indicator of position determined based on an output of a GPS sensor associated with the host vehicle. In some embodiments, the position information may include at least one indicator of position determined based on an output of at least one ego motion sensor associated with the host vehicle. In some embodiments, the position information may be based on a combination of data from two or more sensors. For example, the position information may include at least one indicator of position determined based on a combination of an output of a GPS sensor associated with the host vehicle and an output of at least one ego motion sensor associated with the host vehicle.

In step 3160, process 3100 may include causing transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle. The remotely located entity may be any entity capable of receiving and processing data. For example, step 3160 may include transmitting drive information to server 2910, as described above. The drive information may include the identified at least one two-dimensional feature point, the identified three-dimensional feature points, and the position information. In some embodiments, the amount of information transmitted to the remotely-located entity may be limited or specified, as described above. For example, process 3100 may include identifying and transmitting to the remotely-located entity no more than between 1 and 40 three-dimensional feature points per meter of the road segment. This value may vary depending on the road segment, the capabilities of the host vehicle, the needs of the remotely-located entity, or other factors. In some embodiments, the number of three-dimensional points may be defined by the remotely-located entity. For example, process 3100 may include receiving a request from the remotely-located entity specifying a number of three-dimensional points per meter of the road segment to be identified and/or transmitted.

The remotely-located entity may be configured to align information from multiple vehicles, as described above with respect to FIG. 20 . Accordingly, the remotely-located entity may include one or more processors configured to receive, in addition to the drive information transmitted by the host vehicle, drive information for the road segment from each of a plurality of other vehicles. The drive information for the road segment received from each of the plurality of other vehicles may include at least one position descriptor associated with each detected semantic feature, three-dimensional feature points for detected objects, and position information associated with captured images. In some embodiments, the remotely-located entity may be configured to align information from multiple drives in the same direction of travel. For example, the drive information for the road segment received from each of the plurality of other vehicles may have originated from drives in which the plurality of other vehicles was traveling in a same direction as the host vehicle. The remotely-located entity may be configured to align the drive information along the same direction of travel based on two-dimensional points, as described above. Accordingly, the one or more processors may further be configured to align one or more aspects of the drive information from the host vehicle and the drive information from the plurality of other vehicles based on the identified at least one position descriptor associated with each of the detected one or more semantic features received from the host vehicle and based on the identified at least one position descriptor associated with each of the detected one or more semantic features received as part of the drive information received from each of the plurality of other vehicles.

In some embodiments, the remotely-located entity may be configured to align drive information from opposing directions. For example, the drive information for the road segment received from each of the plurality of other vehicles may have from drives in which at least some of the plurality of other vehicles were traveling in a same direction as the host vehicle and in which at least some of the plurality of other vehicles were traveling in a direction opposite to the host vehicle. The one or more processors may further be configured to align one or more aspects of the drive information from the host vehicle and the drive information from the plurality of other vehicles, including the other vehicles that traveled the road segment in a direction opposite to the host vehicle, based on the three-dimensional feature points received from the host vehicle and based on the three-dimensional feature points received from each of the plurality of other vehicles. In some embodiments, the alignment of one or more aspects of the drive information from the host vehicle and the drive information from the plurality of other vehicles may include correlating 3D feature points acquired during drives of opposite direction, as described above. The one or more processors may further be configured to generate a sparse map based on the aligned drive information from the host vehicle and the drive information from the plurality of other vehicles.

FIG. 32 is a flowchart showing an example process 3200 for creating maps used in navigating autonomous or partially autonomous vehicles, consistent with the disclosed embodiments. Process 3200 may be performed by at least one processing device of a remotely located entity, such as server 2910, as described above. In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 3200. Further, process 3200 is not necessarily limited to the steps shown in FIG. 32 , and any steps or processes of the various embodiments described throughout the present disclosure may also be included in process 3200, including those described above with respect to FIGS. 27-31 .

In step 3210, process 3200 may include receiving first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction. The first drive information may include a first plurality of three-dimensional feature points associated with objects detected by navigation systems of the first plurality of vehicles. For example, step 3210 may include receiving drive information including 3D points 2922, 2924, and 2926 transmitted by host vehicle 2920, as described above with respect to FIG. 29 . the first drive information may be collected by a host vehicle according to process 3100 described above.

In step 3220, process 3200 may include receiving second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction. Similar to the first drive information, the second drive information may include a second plurality of three-dimensional feature points associated with objects detected by navigation systems of the second plurality of vehicles. For example, step 3220 may include receiving 3D points 2932, 2934, and 2936 from host vehicle 2930, as described above with respect to FIG. 29 .

The three-dimensional feature points of the first drive information and the second drive information may be associated with one or more of edges or corners of at least one surface associated with the objects detected by navigations systems of the first and second plurality of vehicles. For example, the three-dimensional feature points received from the first and second plurality of vehicles may be collected similar to 3D point 2812, as described above. For example, the first plurality and the second plurality of three-dimensional feature points may include an x-y position relative to at least one image along with an indicator of range relative to a camera. The indicator of range may be determined based on tracking across two or more plurality of images of an image position associated with an identified object, and an ego motion signal of a host vehicle. For example, the first and second pluralities of points may include x-y positioning of point 2712, as shown in FIG. 27 , as well as depth d, as shown in FIG. 28 . In some embodiments, the first plurality and the second plurality of three-dimensional feature points include an X-Y-Z position relative to a predetermined origin. For example, the predetermined origin may be based on a host vehicle, a sparse map, a real-world object (e.g., a survey marker), a segment of a road, or the like. As described above with respect to FIG. 28 , the X-Y-Z position may be determined based on tracking across two or more plurality of images of an image position associated with an identified object and an ego motion signal of a host vehicle.

In step 3230, process 3200 may include correlating one or more of the first plurality of three-dimensional feature points with one or more of the second plurality of three-dimensional feature points. This correlation may occur in various ways, as described above with respect to FIG. 30 . In some embodiments, the correlation may be based on an iterative closest point algorithm, or similar algorithms for correlating points within two point clouds.

In step 3240, process 3200 may include generating a sparse map based on the correlation of the first plurality of three-dimensional feature points and the second plurality of three-dimensional feature points. The sparse map may include drive information from the first and second plurality of vehicles from different direction that is aligned based on the correlation. For example, the sparse map may include at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction. Accordingly, the sparse map may be used by autonomous or semiautonomous vehicles for navigating the lanes of travel in either direction.

As discussed above, the sparse map may require a relatively large set of 3D points to accurately correlate the data from opposing drive directions. For example, the sparse map may be based on 100 three-dimensional feature points per meter of the road segment, 200 three-dimensional feature points per meter of the road segment, 300 three-dimensional feature points per meter of the road segment, or any other density of points that may allow a statistically significant correlation between points. In some embodiments, the collection of points may be crowd-sourced among several vehicles. Accordingly, the number of points collected per vehicle may be limited. For example, the first drive information and the second drive information may include no more than between 1 and 40 three-dimensional feature points received from any one of the first plurality of vehicles or the second plurality of vehicles per meter of the road segment. The number of points acquired by each vehicle per meter of the road segment may be configurable and/or variable, as described above.

In some embodiments, the sparse map may further be generated based on position descriptors (which may include 2D and/or 3D points) collected by the vehicles. For example, the first drive information may include a first plurality of identified position descriptors associated with objects detected by navigation systems of the first plurality of vehicles, and the second drive information may include a second plurality of identified position descriptors associated with objects detected by navigation systems of the second plurality of vehicles. In some embodiments, the identified position descriptors may include two-dimensional points, which may be collected as described above for 2D point 2712. For example, the first plurality and the second plurality of identified position descriptors may include an x-y position relative to at least one captured image. The generation of the sparse map may further be based on a correlation of the first plurality of identified position descriptors and the second plurality of identified position descriptors determined as part of process 3200.

As described above, the sparse map may further be generated based on position information included in the drive information. Accordingly, the first drive information may include first camera position information associated with a first plurality of captured images, and the second drive information may include second camera position information associated with a second plurality of captured images. The position information may be collected by the first and second plurality of vehicles using one or more sensors, such as position sensor 2824. For example, the first camera position information and the second camera position information may include at least one indicator of position determined based on an output of a GPS sensor, an output of an ego motion sensor, a combination of an output of a GPS sensor and an output of at least one ego motion sensor, or any other sensors or combinations of sensors that may indicate vehicle position. The generation of the sparse map may further be based on the first camera position information and the second camera position information.

Two-Way Aligned Trajectories

As described above, a sparse map may be generated such that drive information from opposing directions of travel is aligned within the sparse map. For example, a sparse map having two-way aligned trajectories may be generated based on crowd-sourced 3D points collected by a plurality of vehicles, as described above. In some embodiments, vehicles may be equipped with rear-facing cameras that may be used to correlate objects viewed in either direction, as described in further detail below (see, e.g., FIGS. 45-48 and associated descriptions). A sparse map having aligned trajectories for each direction of travel may improve navigation for vehicles traversing the roadway. For example, a vehicle may be able to navigate based on the trajectory it is currently traveling along, but may also be able to make determinations in relation to other objects based on the trajectory for the opposing direction of travel. For example, the navigation system of a host vehicle may be configured to assess whether an oncoming vehicle is diverging from a particular trajectory, which may inform navigation action decisions by the host vehicle.

FIG. 33 is an illustration of an example road segment 3300 along which a host vehicle may navigate, consistent with the disclosed embodiments. Road segment 3300 may include one or more lanes of travel, such as lanes 3302 and 3304. At least some of the lanes along road segment 3300 may be associated with opposite directions of travel. For example, lane 3302 may be associated with an opposite direction of travel than lane 3304 along the road segment. As shown in FIG. 33 , a host vehicle 3310 may be traveling along the road segment in lane 3302. Host vehicle 3310 may be an autonomous or semiautonomous vehicle, consistent with the disclosed embodiments. Host vehicle 3310 be the same as or similar to vehicle 200 described herein. Accordingly, any of the descriptions or disclosures made herein in reference to vehicle 200 may also apply to host vehicle 3310, and vice versa.

Host vehicle 3310 may be configured to capture images of an environment of the host vehicle. For example, host vehicle 3310 may capture images using one or more image capture devices (e.g., cameras), such as image capture device 122, image capture device 124, and image capture device 126. Host vehicle 3310 may further be configured to detect one or more objects or features within road segment 3300 based on the images. For example, road segment 3300 may include road signs 3332 and 3334, lane mark 3336, directional arrow 3338, or various other features that may be recognized by host vehicle 3310. Host vehicle 3310 may be configured to navigate along road segment 3300 based on detected road features and objects, as described throughout the present disclosure. Road segment 3300 may further include a target vehicle 3320 traveling in lane 3304 in a direction opposite of host vehicle 3310.

FIG. 34 illustrates an example sparse map 3400 having two-way aligned trajectories, consistent with the disclosed embodiments. Sparse map 3400 may include drive information associated with road segment 3300, as shown in FIG. 34 . In some embodiments, spares map 3400 may include additional drive information beyond what is shown in FIG. 34 . For example, FIG. 34 may show a portion of sparse map 3400 relevant to road segment 3300. The drive information may have been collected previously by one or more vehicles traveling along road segment 3300 and transmitted to a server 3410. In some embodiments, server 3410 may correspond to sever 1230, as described above. Accordingly, any of the descriptions or disclosures made herein in reference to server 1230 may also apply to server 3410, and vice versa. Server 3410 may be configured to generate sparse map 3400 based on the received drive information. For example, server 3410 may collect drive information from a plurality drives, align information from the plurality of drives, and generate sparse map 3400 based on the aligned drive information, as described above (e.g., with respect to FIGS. 12-18 ). Server 3410 may also transmit the sparse map (or data including updates to the sparse map) to one or more autonomous or semi-autonomous vehicles, such as host vehicle 3310.

As shown in FIG. 34 , sparse map 3400 may include one or more mapped navigational features associated with road segment 3300. In some embodiments, sparse map 3400 may include a target trajectory 3402 associated with lane 3302, and a target trajectory 3404 associated with lane 3304. Target trajectories 3402 and 3404 may be generated based on drive information from previous traversals of road segment 3300 and may be stored in association with sparse map 3400 as three-dimensional splines. Sparse map 3400 may further include mapped road features generated based on drive information previously collected from vehicles traversing road segment 3300. The mapped road features may correspond to objects or road features identified by the vehicles that previously traversed road segment 3300. For example, mapped navigational feature 3424 may correspond to road sign 3334 (or a corner, edge, or other feature of road sign 3334). Similarly, mapped navigational feature 3434 may correspond to directional arrow 3338, mapped road features 3426 and 3436 may correspond to road sign 3332, and mapped road features 3422 and 3432 may correspond to lane marks along road segment 3300. In some embodiments, the mapped road features may include type classifiers indicating a type of the road feature. For example, mapped road feature 3424 may include a type classifier indicating it is associated with a road sign. Various other classifiers or information may also be associated with the mapped road features.

In some embodiments, the mapped road features may have been collected by vehicles travelling in opposite directions along road segment 3400. For example, mapped road features 3422, 3424, and 3426 may have been collected by one or more vehicles traveling along lane 3302 and mapped road features 3432, 3434, and 3436 may have been collected by one or more vehicles traveling along lane 3304. The mapped road features from opposite directions may be aligned using various techniques disclosed throughout the present disclosure. For example, sparse map 3400 may include two-way alignment of mapped road features based on crowd-sourced 3D points as described above. In some embodiments, sparse map 3400 may include two-way alignment of mapped road features based on forward and rear facing cameras, as described in greater detail below.

Host vehicle 3310 may be configured to receive sparse map 3400 from server 3410. In some embodiments, host vehicle 3310 may be configured to receive update data associated with sparse map 3400. For example, as server 3410 acquires additional drive information, location data for target trajectories and/or mapped road features may be refined, and the refined data may be transmitted to host vehicle 3310. Host vehicle 3310 may be configured to capture one or more images of road segment 3310 and determine navigational actions based on the captured images and sparse map 3400. For example, host vehicle 3310 may capture an image including a representation of road sign 3334 and may identify one or more features associated with road sign 3334 in the images. Host vehicle 3310 may determine that the identified features are associated with mapped road feature 3424 and may determine a position of host vehicle 3310 relative to target trajectory 3402. Host vehicle 3310 may determine a navigation action based on the determined location. For example, this may include a steering maneuver to align host vehicle with target trajectory 3402. Various other navigations may be performed, such as a braking maneuver, an acceleration maneuver, a lane change maneuver, maintaining a current course of action or speed, or the like.

In some embodiments, host vehicle 3310 may further determine navigations based on mapped navigational features in sparse map 3400 that were captured by vehicles traveling in an opposite direction as host vehicle 3310. For example, host vehicle 3310 may identify features such as directional arrow 3434 or road sign 3332 that may be associated with mapped road features 3434 and 3436, respectively. As with the mapped road features captured by vehicles traveling the same direction as host vehicle 3310, these mapped road features captured be vehicles from the opposite direction may be used for navigation by host vehicle 3310. Accordingly, sparse map 3400 may provide a more robust collection of mapped navigational features as compared to a sparse map where drive information from multiple directions of travel has not been aligned.

Further, host vehicle 3310 may assess other conditions of road segment 3300 based on sparse map 3400. For example, host vehicle 3310 may identify a representation of an oncoming target vehicle 3320 within one or more captured images. Based on a current position of host vehicle 3310 relative to sparse map 3400 and a position of target vehicle 3320 relative to host vehicle 3310 determined based on analysis of the images, host vehicle 3310 may determine a position of target vehicle 3320 relative to target trajectory 3404. Host vehicle 3310 may further determine navigation actions based on the position of target vehicle 3320 relative to target trajectory 3404. If target vehicle 3404 is traveling along a path that is not consistent with target trajectory 3404, this may indicate a navigation action should be taken by host vehicle in response. For example, host vehicle 3310 may determine that target vehicle 3320 is veering from target trajectory 3404 in a direction towards host vehicle 3310. This may indicate that a driver of target vehicle 3320 is distracted or has fallen asleep, that there is an obstacle in lane 3304, that poor road conditions exist (which may also be present in lane 3302 ahead of host vehicle 3310), or other conditions that may cause target vehicle 3320 to veer off course. Accordingly, host vehicle 3310 may perform an avoidance maneuver, such as diverging from target trajectory 3402 to avoid a collision, perform an acceleration of host vehicle 3310, perform a braking maneuver, provide a warning indication (e.g., flashing headlights, flashing high-beam lights, sounding a horn), or the like. In some embodiments, host vehicle 3310 may report target vehicle 3320. For example, if target vehicle 3320 veers from target trajectory 3404 it may indicate that a drive of target vehicle 3320 is intoxicated and host vehicle 3310 may flag target vehicle 3320 for other vehicles traversing road segment 3300, notify local authorities, etc.

Various other determinations may be made by host vehicle 3310 based on target trajectory 3404 being aligned with target trajectory 3402 within sparse map 3400. For example, host vehicle 3310 may recognize that target trajectory 3404 is associated with a drivable road surface accessible to host vehicle 3310. For example, in case of an emergency (e.g., a pedestrian or animal running into lane 3302, a collision between vehicles ahead of host vehicle 3310, or other unexpected situations), host vehicle 3310 may veer temporarily onto target trajectory 3310, especially if no oncoming vehicles are detected. As another example, host vehicle 3310 may use target trajectory 3404 when making a U-turn into lane 3404. In some embodiments, host vehicle 3310 may warn target vehicle 3320 or other entities of various conditions of road segment 3300. For example, host vehicle may recognize an obstacle along target trajectory 3404 (e.g., a pedestrian, poor road conditions, a rock or other debris, etc.) and may warn target vehicle 3320 of the conditions. This may include flashing headlights, sounding a horn, transmitting a notification signal, or other forms of warning indications. If target vehicle 3320 (or a driver of target vehicle 3320) is unable to recognize the condition, the warning from host vehicle 3310 may prompt a response from target vehicle 3320.

FIG. 35 is a flowchart showing an example process 3500 for navigating an autonomous or partially autonomous host vehicle, consistent with the disclosed embodiments. Process 3500 may be performed by at least one processing device of a host vehicle, such as processing device 110. In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 3500. Further, process 3500 is not necessarily limited to the steps shown in FIG. 35 , and any steps or processes of the various embodiments described throughout the present disclosure may also be included in process 3500, including those described above with respect to FIGS. 33 and 34 . In some embodiments, some or all of process 3100 may be performed in conjunction with process 3500.

In step 3510, process 3500 may include receiving, from an entity remotely located relative to the host vehicle, a sparse map associated with at least one road segment. For example, host vehicle 3310 may receive sparse map 3400 from server 3410, as described above. Accordingly, host vehicle 3310 may be equipped with an electronic communications device, such as wireless transceiver 172. In some embodiments, host vehicle 3310 may be configured to store sparse map 3400 in a local storage, such as map database 160. The sparse map may include various information associated with the at least one road segment. In some embodiments, the sparse map may include a first plurality of mapped navigational features generated based on drive information previously collected from a first plurality of vehicles that traveled in a first direction along the at least one road segment. For example, the sparse map may include mapped road features 3422, 3424, 3426, and/or target trajectory 3402. The sparse map may further include a second plurality of mapped navigational features generated based on drive information previously collected from a second plurality of vehicles that traveled in a second direction along the at least one road segment. For example, the sparse map may include mapped road features 3432, 3434, 3436, and/or target trajectory 3404. The first plurality of mapped navigational features and the second plurality of mapped navigational features may be correlated within a common coordinate system, as described above.

In step 3520, process 3500 may include receiving, from a camera associated with the host vehicle, a first plurality of images and a second plurality of images representative of an environment of the host vehicle as the host vehicle travels along the at least one road segment in the first direction. For example, host vehicle 3310 may capture images using image capture devices 122, 124, and 126 when traveling along lane 3302 of road segment 3300.

In step 3530, process 3500 may include determining a first navigational action for the host vehicle based on analysis of at least one of the first plurality of images and based on the first plurality of mapped navigational features. In some embodiments, the first navigational action may align the host vehicle with a target trajectory. For example, the first plurality of mapped features may include a target trajectory for a lane of travel along the at least one road segment in the first direction and at least one mapped road feature. The mapped road feature may include a location of the at least one mapped road feature and a type classifier associated with the at least one mapped road feature, as described above. Process 3500 may further include localizing the host vehicle relative to the target trajectory based on an identification of a representation in the first plurality of images of the at least one mapped road feature. For example, host vehicle 3310 may identify a representation of road sign 3334 within the plurality of images and may correlate it with mapped road feature 3424. Accordingly, process 3500 may include determining a position of the host vehicle relative to the sparse map based on the plurality of images.

In step 3540, process 3500 may include causing one or more actuators associated with the host vehicle to implement the first navigational action. For example, this may include performing a steering maneuver, a braking maneuver, an acceleration maneuver, maintaining a current heading direction or the like. In some embodiments, the first navigational action ay include a change in heading direction of the host vehicle to reduce a difference between an actual trajectory of the host vehicle and the target trajectory.

In step 3550, process 3500 may include determining a second navigational action for the host vehicle based on analysis of the second plurality of images and based on the second plurality of mapped navigational features. In some embodiments, step 3550 may include determining whether a target vehicle is traveling along a path consistent with a target trajectory included in the sparse map. For example, process 3500 may include identify a target vehicle represented in the second plurality of images, such as target vehicle 3320. Process 3500 may include determine, based on analysis of one or more of the second plurality of images, whether the target vehicle is traveling along a path consistent with the target trajectory for the lane of travel along the at least one road segment in the second direction. For example, process 3500 may include determining whether target vehicle 3320 has veered from target trajectory 3404.

In step 3560, process 3500 may include causing the one or more actuators associated with the host vehicle to implement the second navigational action. As with the first navigational action, the second navigational action may include performing a steering maneuver, a braking maneuver, an acceleration maneuver, maintaining a current heading direction or the like. In some embodiments, the second navigational action may be different than the first navigational action. In some embodiments, the second navigational action may be foregoing or canceling the first navigational action. As described above, process 3500 may include determining whether a target vehicle is traveling along a path consistent with the target trajectory for the lane of travel along the at least one road segment in the second direction. Accordingly, step 3560 may include determining the second navigational action based on whether the target vehicle is determined to be traveling along a path consistent with the target trajectory for the lane of travel along the at least one road segment in the second direction. In some embodiments, the second navigational action may include maintaining heading and speed of the host vehicle in response to a determination that the target vehicle is traveling along a path consistent with the target trajectory. Conversely, the second navigational action may include at least one of slowing the host vehicle or changing a heading of the host vehicle in response to a determination that the target vehicle is traveling toward the host vehicle and not along a path consistent with the target trajectory.

Fully Aligned Junctions

As described throughout the present disclosure, sparse maps or other road navigational maps may be generated based on crowd-sourced data collected by vehicles traversing a roadway. As indicated above, without the advanced techniques for collecting and correlating drive information captured by the vehicles disclosed herein, it may be difficult or impossible to align drive information from multiple directions of travel along a road segment. For example, road features or objects may look different from opposite viewing angles and may appear in different portions of an image. Accordingly, it may be difficult for a system to correlate features identified based on 2D image coordinates of front-facing cameras alone. For some use cases, sparse maps without two-way aligned trajectories may be sufficient for navigating along a segment of a roadway.

However, sparse maps with target trajectories aligned in a single direction of travel may not be sufficient for navigating within a junction, such as an intersection. It may be beneficial for trajectories within for junctions to be fully aligned because a host vehicle may travel in any available direction offered by the junction. For example, target trajectories for left turns, right turns, u-turns, or traveling through the junction (including multiple possible lanes entering and exiting in each direction) may need to be fully aligned to allow vehicles to precisely navigate through the junction. This may be especially important in junctions where vehicles may be traveling along multiple trajectories through the intersection at the same time. For example, a junction may include a stop light in which vehicles entering the junction from opposite directions are signaled to make left turns at the same time. To avoid a collision with oncoming turning vehicles, the target trajectories associated with each of the left turns may need to be accurately aligned.

Similar to with segments of a roadway, 3D points may be collected and correlated as described above. With respect to road junctions, however, this may include crowd sourcing and aligning drive information from multiple vehicles traversing the same entrance and exit combination through the junction. Then, the aligned drive information from each entrance and exit combination may be correlated such that a sparse map with a fully-aligned trajectories for each entrance and exit combination is generated.

FIG. 36A illustrates an example junction 3600 that may be traversed by one or more vehicles, consistent with the disclosed embodiments. As shown in FIG. 36A, junction 3600 may be an intersection of two roadways, each having one lane of travel in each direction. As used herein, a junction may include any point or region in which two or more roadways are joined. Accordingly, while junction 3600 is shown by way of example, it is to be understood that the disclosed systems and methods may apply in various other junction configurations. For example, junctions in accordance with the present disclosure may include but are not limited to four-way intersections, T-junctions, Y-intersections, traffic circles (or “roundabouts”), road forks, turn lanes, parking lots, or any other region with multiple traversable roadways. In some embodiments, junctions may be controlled, for example, through the use of stop signs, yield signs, traffic lights, or forms of control apparatuses. In some embodiments, junctions may be uncontrolled, where no signals or signs are included to indicate right-of-way.

In the example shown in FIG. 36A, junction 3600 may include one or more traffic lights, such as traffic signals 3604 and 3606. Various other objects, such as road sign 3602 may also be included within a vicinity of junction 3600, as shown. While junction 3600 is shown as including two traffic signals for purposes of simplicity, it is to be understood that junction 3600 may include additional traffic signals, road signs, lane markings, or other road features. Further, while junction 3600 is shown as an intersection of two roadways with single lanes of travel in each direction, it is to be understood that junction 3600 may include any number of roadways or lanes of travel.

Vehicles, such as host vehicle 3610 may traverse junction 3600 through various combinations of entrance and exit points. FIG. 36B illustrates various entrance and exit combinations for junction 3600 that may be traveled by host vehicle 3610, consistent with the disclosed embodiments. In particular, as shown in FIG. 36B, junction 3600 may include a plurality of entrance points 3620A, 3620B, 3620C, and 3620D through which a vehicle may enter junction 3600. Further, junction 3600 may include a plurality of exit points 3630A, 3630B, 3630C, and 3630D, through which a vehicle may exit junction 3600. A plurality of possible trajectories through which a vehicle may traverse junction 3600 may be defined by the entrance and exit points. In particular, for every possible combination of one of entrance points 3620A, 3620B, 3620C, and 3620D with one of exit points 3630A, 3630B, 3630C, and 3630D, a target trajectory may be defined. For example, if host vehicle 3610 enters junction 3600 through entrance point 3620A, a right-turn trajectory 3642 may be available for exiting through exit point 3630B, a straight trajectory 3644 may be available for exiting through exit point 3630C, and a left-turn trajectory 3646 may be available for exiting through exit point 3630D. Although not shown in FIG. 36B, a u-turn target trajectory may also be defined for entering through entrance point 3620A and exiting through exit point 3630A. Similar combinations of trajectories may be defined for each of entrance points 3620B, 3620C and 3620D. As noted above, the disclosed systems and methods may apply to other configurations of junctions, including junctions with additional lanes in each direction. Further, junction 3600 may include turn lanes or other features that may increase the number of entrance and exit points. As the number of entrance and/or exit points increases, the number of target trajectories defined by junction 3600 may also increase (and vice versa).

As host vehicle 3610 and other host vehicles traverse junction 3600, they may collect drive information including road features associated with junction 3600. FIG. 37 illustrates example three-dimensional points that may be collected by host vehicle 3610 while traversing junction 3600, consistent with the disclosed embodiments. For example, host vehicle 3610 may travel through junction along target trajectory 3646, as described above. While target trajectory 3646 is shown by way of example, similar drive information may be collected along other target trajectories, including those shown in FIG. 36B. Host vehicle 3610 may be configured to capture a plurality of images of the environment of host vehicle 3610. For example, host vehicle 3610 may capture images using one or more image capture devices (e.g., cameras), such as image capture device 122, image capture device 124, and image capture device 126. Host vehicle 3610 may be an autonomous or semiautonomous vehicle, consistent with the disclosed embodiments. Host vehicle 3610 be the same as or similar to vehicle 200 described herein. Accordingly, any of the descriptions or disclosures made herein in reference to vehicle 200 may also apply to host vehicle 3610, and vice versa.

Host vehicle 3610 may further be configured to detect one or more objects or features within junction 3600 based on the images. For example, host vehicle 3610 may identify one or more features of road sign 3602, traffic signals 3604 and 3606, or other objects or features within junction 3600. Host vehicle 3610 may be configured to determine three dimensional points associated with the detected features. For example, host vehicle 3610 may identify 3D point 3702 associated with road sign 3602, 3D points 3704, 3706, 3708, and 3710 associated with lights or other features of traffic signals 3604 and 3606, 3D point 3712 associated with a lane mark of junction 3600, or various other 3D points. 3D points 3702, 3704, 3706, 3708, 3710, and 3712 may be represented in various ways. For example, the 3D points may include an x-y position relative to one or more captured images along with an indicator of range relative to the camera. The range may be determined based on tracking a position of an identified object over two or more images, along with an ego motion signal of the host vehicle (e.g., using structure from motion (SfM) techniques, etc.). In some embodiments, the 3D points may be represented as X-Y-Z coordinates of a real-world coordinate system. Additional details regarding capturing 3D points from a plurality of images are provided above with respect to FIG. 28 .

Host vehicle 3610 may further be configured to transmit drive information (including captured 3D points 3702, 3704, 3706, 3708, 3710, and 3712) to a remotely-located entity, such as server 3720. Server 3720 may be configured to generate a sparse map based on drive information associated with junction 3600 from a plurality of vehicles. Server 3720 may also transmit the sparse map (or data indicating updates to the sparse map) to one or more autonomous or semi-autonomous vehicles, which may be used for navigation. In some embodiments, server 3720 may correspond to sever 1230, as described above. Accordingly, any of the descriptions or disclosures made herein in reference to server 1230 may also apply to server 3720, and vice versa.

Server 3720 may receive three dimensional points from a plurality of vehicles traveling through junction 3600 along the same trajectory and may align mapped road features captured by the plurality of vehicles to generate an aligned group of three-dimensional points for each entrance and exit combination. FIG. 38 illustrates an example alignment of three-dimensional points collected along a common target trajectory, consistent with the disclosed embodiments. As shown in FIG. 38 , server 3720 may receive 3D points 3802, 3804, 3806, 3808, 3810, and 3812 captured by a second host vehicle traveling through junction 3600 along target trajectory 3646. Server 3720 may correlate the three-dimensional points captured from multiple drives to generate an aligned 3D feature point group 3800 associated with target trajectory 3646. The alignment may be performed using an iterative closest point (ICP) algorithm, or other algorithms configured to align multiple point clouds. Server 3720 may translate, rotate, scale, or otherwise transform the various groups of three-dimensional points collected from different vehicles to generate aligned 3D feature point group 3800. For example, 3D point 3712 may be aligned with 3D point 3812, as shown in FIG. 38 . While the alignment process is shown in FIG. 28 based on groups of 3D point data collected by two vehicles, similar alignment may be performed for data from three or more drives.

In some embodiments, one or more 3D points within aligned 3D feature point group 3800 may be combined. For example, rather than including multiple points 3712 and 3812, aligned 3D feature point group 3800 may include a single combined 3D point. Accordingly, aligned 3D feature point group 3800 may include a group of combined 3D feature points, each of the combined points being associated with one mapped road feature in junction 3600. The combined points may represent an average location among correlated 3D points associated with the same road feature. In some embodiments the average may be weighted based on a level of confidence or other value associated with a collected 3D point. For example, points collected during heavy fog or storms may be assigned a lower confidence level than points collected during the day. Various other factors, such as time of day, camera properties (e.g., resolution, etc.), vehicle speed, or any other factors that may affect the accuracy of determining 3D points may also be used to define a confidence level. In some embodiments, generating aligned 3D feature point group 3800 may include additional processing operations. For example, server 3720 may exclude outlier 3D points (e.g., 3D points associated with locations differing from locations of other 3D points by more than a threshold amount), or other forms of processing.

To generate an aligned sparse map for junction 3600, server 3720 may then correlate 3D points from aligned 3D feature point group 3800 with 3D points from aligned 3D feature point groups associated with other entrance-exit combinations. FIG. 39 illustrates an example process for generating a sparse map 3940 based on correlated 3D points from multiple entrance-exit combinations, consistent with the disclosed embodiments. Server 3720 may generate a plurality of aligned 3D feature point groups 3900 based on each entrance-exit combination for junction 3600. For example, aligned 3D feature point group 3902 may correspond to aligned 3D feature point group 3800 and may be generated as described above with respect to FIG. 38 . Aligned 3D feature point groups 3904 and 3906 may correspond to trajectories 3644 and 3642, respectively. Aligned 3D feature point groups for each other entrance-exit combination may be generated as well, similar to the process described above for aligned 3D feature point group 3800. For example, aligned 3D feature point group 3908 may correspond to a trajectory based on entering junction 3600 at entrance point 3620C and exiting junction 3600 at exit point 3630B, and so on. Although not shown in FIG. 39 , aligned 3D feature point groups 3900 may further include aligned 3D feature point groups associated with U-turns or other trajectories through junction 3600, as noted above.

Server 3720 may correlate aligned 3D points from each of aligned 3D feature point groups 3900 to generate a sparse map 3940 having fully aligned trajectories for each entrance-exit combination associated with junction 3600 (or a selected subset of entrance-exit combinations). FIG. 40 illustrates an example alignment of two sets of aligned 3D points, consistent with the disclosed embodiments. For example, FIG. 40 may show an alignment process for aligned 3D feature point group 3902, which may be associated with target trajectory 3646, and aligned 3D feature point group 3908, which may be associated with target trajectory 4010, as shown. Target trajectory 4010 may be a trajectory defined based on entering junction 3600 at entrance point 3620C and exiting junction 3600 at exit point 3630B. Server 3720 may determine a correlation between aligned 3D feature point 4012 (from aligned 3D feature point group 3902) and aligned 3D feature point 4022 (from aligned 3D feature point group 3908). Based on the correlation, server 3720 may transform one or more of aligned 3D feature point groups 3902 and 3802 and the associated trajectories to align the points, as shown in FIG. 40 . This may include applying an iterative closest point (ICP) algorithm, or other algorithms configured to align multiple point clouds. The correlation and alignment process may be similar to the correlation and alignment process described above with respect to FIG. 30 . Accordingly, any of the details or descriptions provided above may also apply to FIG. 40 .

As a result, target trajectories 3646 and 4010 may be aligned, based on the aligned crowd-sourced 3D points for each entrance-exit combination, as shown in FIG. 40 . A similar process may be performed for each of aligned 3D feature point groups 3900. Therefore, trajectories for each entrance-exit combination within sparse map 3940 may be aligned with each other. Server 3720 may then transmit sparse map 3940 to one or more vehicles, such as host vehicle 3610, which may use sparse map 3940 for navigating junction 3600. As additional 3D points are collected by vehicles traveling through junction 3600, server 3720 may update sparse map 3940 and may transmit an updated sparse map 3940 (or update data defining changes over a previous sparse map) to one or more vehicles.

FIG. 41 is a flowchart showing an example process 4100 for creating maps used in navigating autonomous or partially autonomous vehicles, consistent with the disclosed embodiments. Process 4100 may be performed by at least one processing device of a remotely located entity, such as server 2910, as described above. In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 4100. Further, process 4100 is not necessarily limited to the steps shown in FIG. 41 , and any steps or processes of the various embodiments described throughout the present disclosure may also be included in process 4100, including those described above with respect to FIGS. 27-31 .

In step 4110, process 4100 may include receiving drive information from each of a plurality of vehicles distributed across a plurality of vehicle groups that traverse a road junction. For example, step 4100 may include receiving drive information associated with junction 3600 described above. Each vehicle group may include one or more vehicles that traverse a different entrance-exit combination associated with the road junction. For example, junction 3600 may be associated with trajectories defined by various combinations of entrance points 3620A, 3620B, 3620C, and 3620D with one of exit points 3630A, 3630B, 3630C, and 3630D, as shown in FIG. 36B. The drive information from each of the plurality of vehicles may include three-dimensional feature points associated with objects detected by analyzing images captured as a particular vehicle traversed a particular entrance-exit combination of the road junction. For example, step 4110 may include receiving drive information from host vehicle 3610 associated with target trajectory 3646, as shown in FIG. 37 . Additional drive information may be collected from other host vehicles as they travel along trajectory 3646 and from host vehicles traveling along other trajectories defined by the entrance-exit combinations.

Each of the three-dimensional feature points may be determined as described above with respect to FIG. 28 . For example, each of the three-dimensional feature points includes an x-y position relative to at least one captured image along with an indicator of range (e.g., depth d) relative to a camera that acquired the at least one captured image. The indicator of range may be determined based on tracking across two or more images of an image position associated with an identified object, and based on an ego motion signal associated with a host vehicle. In some embodiments, each of the three-dimensional feature points may include an X-Y-Z position. Similar to the range, the X-Y-Z position may be determined based on tracking across two or more plurality of images of an image position associated with an identified object, and an ego motion signal of a host vehicle. In some embodiments, collection of the 3D points may be distributed across many vehicles to reduce the computational load on any one vehicle. For example, the drive information from each of the plurality of vehicles may include no more than between 1 and 40 three-dimensional feature points per meter of the road junction, or other suitable numbers of feature points.

In step 4120, process 4100 may include, for each of the entrance-exit combinations, align the three-dimensional feature points received in the drive information collected from the one or more vehicles that traversed that entrance-exit combination to generate a plurality of aligned three-dimensional feature point groups. Accordingly, one aligned three-dimensional feature point group may be generated one for each entrance-exit combination of the road junction. For example, for target trajectory 3646, three-dimensional feature points collected by multiple vehicles traveling through junction 3600 along target trajectory 3646 may be aligned as shown in FIG. 38 . Similar alignment may be performed for each entrance-exit combination to generate a plurality of aligned three-dimensional feature point groups 3900, as described above.

In step 4130, process 4100 may include correlating one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included every other aligned three-dimensional feature point group from among the plurality of aligned three-dimensional feature point groups. For example, 3D feature points from each of aligned three-dimensional feature point groups 3900 may be correlated as shown in FIGS. 39 and 40 . This may include applying an iterative closest point algorithm, or other algorithms for aligning 3D point clouds.

In step 4140, process 4100 may include generating a sparse map based on the correlation of the one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included every other aligned three-dimensional feature point group. For example, step 4140 may include generating sparse map 3940, as described above. Accordingly, the sparse map may include at least one target trajectory associated with each of the entrance-exit combinations of the road junction. In some embodiments, the sparse map may further include one or more mapped junction features determined based on the drive information received from one or more of the plurality of vehicles. For example, the one or more mapped junction features include at least one of: a traffic light, stop sign, stop line, light pole, road marking, crosswalk, building, or curb. As discussed above, the sparse map may require a relatively large set of 3D points to accurately correlate the trajectories. For example, the sparse map may be based on 100 three-dimensional feature points per meter of the road segment, 200 three-dimensional feature points per meter of the road segment, 300 three-dimensional feature points per meter of the road segment, or any other density of points that may allow a statistically significant correlation between points.

In some embodiments, the sparse may be further be generated based on camera position information associated with the captured 3D points. For example, the drive information may include camera position information associated with a plurality of captured images and the generation of the sparse map may further be based on the camera position information. The camera position may be based on an output of one or more position sensors, such as position sensor 2824 described above. For example, the camera position information may include at least one indicator of position determined based on an output of a GPS sensor, based on an output of an ego motion sensor, based on a combination of an output of a GPS sensor and an output of at least one ego motion sensor, or various other forms of sensors or combinations thereof.

Rear-Facing Camera for Two-Way Sparse Map Alignment

As described above, when aligning information from vehicles moving in the same direction of travel along the road segment, 2D points can be used to generate the sparse map. However, these 2D points may not be sufficient for aligning multiple drives in different directions because the same road segment may look completely different when viewed from the same direction. For example, the same road sign when viewed from one direction may look completely different from the opposite direction. Therefore, it may be difficult for a system to correlate points representing the road sign from one direction, with points representing the sign collected from the other direction. Accordingly, in order to align drive data from opposing directions of travel, some form of “link” may be used to correlate the collected points.

One solution, described above, may be to collect 3D points from a plurality of vehicles, which can more accurately be aligned across multiple driving directions. An alternative solution includes collecting images of road feature from a rearward facing camera. For example, a vehicle may capture images of a front side of an object using a forward facing camera and later capture images of the back side of an object. Feature points associated with the front side of the object may then be correlated with feature points associated with the rear side of the object, based on analysis of the images. This information may be useful in aligning drives from different directions. For example, a second host vehicle traveling in an opposite direction of travel from the host vehicle may capture images of the back side of the object using a front-facing camera. Feature points identified by the second host vehicle may be correlated with feature points collected by the first host vehicle based on a known relationship between the back side of the object and the front side of the object.

As another example, a rear facing camera can be used to detect drive information as the host vehicle traverses a road segment, similar to a front-facing camera. That is, the rear facing camera can operate in the same way a forward facing camera of a car traveling in a direction opposite to the host vehicle would operate. The drive information associated with the front-facing camera and the drive information from the rear-facing camera may be used to align drive information collected by front-facing cameras of host vehicles traveling in opposite directions along the same road segment.

FIG. 42A illustrates an example road segment 4200 along which drive information from multiple directions may be aligned using rear-facing cameras, consistent with the disclosed embodiments. As shown in FIG. 42A, road segment 4200 may include multiple lanes, such as lanes 4222 and 4224. Lanes 4222 and 4224 may be associated with different directions of travel. Road segment 4200 may include one or more objects that may be identified by host vehicles traversing road segment 4200, such as host vehicle 4210. Host vehicle 4210 may be an autonomous or semiautonomous vehicle, consistent with the disclosed embodiments. Host vehicle 4210 may be configured to capture a plurality of images of the environment of host vehicle 4210. For example, host vehicle 4210 may capture images using a front-facing camera 4212. Front-facing camera 4212 may be any form of image capture device capable of capturing images facing the same direction as a direction of travel of host vehicle 4210. Host vehicle 4210 be the same as or similar to vehicle 200 described herein. Accordingly, any of the descriptions or disclosures made herein in reference to vehicle 200 may also apply to host vehicle 4210, and vice versa. For example, front-facing camera 4212 may correspond to one or more of image capture device 122, image capture device 124, and image capture device 126 described in further detail above.

Host vehicle 4210 may further be configured to detect one or more objects or features within road segment 4200 based on the images. For example, host vehicle 4210 may identify one or more features of road sign 4202, a crosswalk mark 4204, and/or a tree 4206. Road segment 4200 may include other forms of objects and/or features not shown in FIG. 42 that may be detected by host vehicle 4210. For example, this may include but is not limited to lane marks, road boundaries, traffic lights, light posts, potholes, trees, buildings, or other features that may be present along a roadway.

FIG. 42B illustrates an example image 4250 that may be captured by a front-facing camera of host vehicle 4210, consistent with the disclosed embodiments. For example, image 4250 may be captured by front-facing camera 4212 of host vehicle 4210 when traveling along road segment 4200 in the position shown in FIG. 42A. Host vehicle 4210 may identify objects, such as road sign 4202 and crosswalk mark 4204 in image 4250. This may include using various image processing algorithms as described above. Host vehicle 4210 may be configured to identify position descriptors associated with various features in the images. For example, host vehicle 4210 may determine a position descriptor associated with road sign 4204. The position descriptor may include any information at least partially defining a location of the feature. For example, the position descriptor may include 2D point information, which may be represented as x-y image coordinates, as described above with respect to FIG. 27 .

For semantic features, having a predetermined type or other characteristics, host vehicle may be configured to determine an object type classification, as described above. For example, the object type classification may be a value indicating road sign 2710 is a road sign, a speed limit sign, a 30 MPH speed limit sign, or various other classifiers. The object type classification may be a numerical code, an alphanumerical code, a semantic description, or any other suitable value. In some embodiments, a single position descriptor along with the object type classification may be defined for road sign 4202. For example, a point 4252 may be associated with an edge of road sign 4202. Various other point locations may be used, such as a center, a corner, a top or side edge, a base point, or the like. 2D points for non-semantic features may be detected as well. For example, a 2D point may be defined for a corner of crosswalk mark 4204. As described in greater detail above, the non-semantic feature points may be associated with a unique identifier based on applying an image processing algorithm to pixels surrounding the 2D point. Host vehicle may determine other information and/or descriptors associated with the semantic features. For example, this may include height and width information for an identified object, information defining bounding box circumscribing the object in the image, or other information that may identify the object.

In some embodiments, the position descriptors may include 3D points. For example, the position descriptors may include x-y coordinates based on an image (e.g., as defined by the 2D points described above) along with range information indicating a distance from the camera to the object. In some embodiments, the position description may be based on X-Y-Z coordinates based on a real-world origin point. Additional details regarding determining 3D points are provided above with respect to FIG. 28 .

Host vehicle 4210 may also be equipped with a rear-facing camera that may capture images of the environment of host vehicle 4210 as it travels along road segment 4200. FIG. 43A illustrates an example scenario for host vehicle 4210 to capture a rear-facing image along road segment 4200, consistent with the disclosed embodiments. For example, host vehicle 4210 may be equipped with rear-facing camera 4214, as shown in FIG. 43A. Rear-facing camera 4214 may be any form of image capture device configured to capture images from a direction opposite the direction of travel of host vehicle 4210 when moving forward. Rear facing camera 4214 may correspond to one or more of image capture device 122, image capture device 124, and image capture device 126. As shown in FIG. 43A, host vehicle 4210 may have traveled further along road segment 4200 than in the previous position shown in FIG. 42A. For example, host vehicle 4210 may have traveled along lane 4222 past crosswalk mark 4204 and road sign 4202.

FIG. 43B illustrates an example rear-facing image 4350 that may be taken by host vehicle 4210, consistent with the disclosed embodiments. For example, image 4350 may be captured by rear-facing camera 4214 from the position of host vehicle 4210 shown in FIG. 43A. As shown in FIG. 43B, image 4350 may include a back side of road sign 4204 along with crosswalk mark 4204. Lanes 4222 and 4224 are identified in image 4350 for reference. Host vehicle 4210 may be configured to identify position descriptors for one or more features along road segment 4200 based on image 4350. The position descriptors may be 2D feature points (e.g., represented based on x-y coordinates of an image) or 3D feature points (e.g., represented as a 2D point along with a range value, as real-world X-Y-Z coordinates, etc.), as described above. In some embodiments, the road features may include semantic features. Accordingly, the position descriptors may be associated with object type classifications determined by host vehicle 4210. In some embodiments, the features may include non-semantic features. Accordingly, the position descriptors may be associated with unique identifiers determined based on application of an image processing algorithm to pixels surrounding the feature point, as described above.

In the example shown in FIG. 43B, host vehicle may determine a position descriptor 4302 associated with road sign 4202. In some embodiments, position descriptor 4302 may be a semantic point, for example, associated with a back side of a road sign object type. In some embodiments, 2D point 4302 may be a non-semantic point that is identified based on pixels surrounding 2D point 4302 in image 4350 such that it may be identified by other host vehicles in similar images. Similarly, host vehicle 4210 may be configured to identify point 4306 associated with tree 4206.

In some embodiments, host vehicle 4210 may further track position information associated with images 4250 and 4350. For example, host vehicle 4210 maybe equipped with a position sensor, such as position sensor 2824 described above. In some embodiments, the position sensor may be a GPS sensor configured to determine GPS coordinates of host vehicle 4210. In some embodiments, the position sensor may include one or more sensors configured to track an ego motion of host vehicle 4210. For example, the ego motion sensors may include speed sensors, steering alignment sensors, brake sensors, accelerometers, compasses, or other sensor that may be used to track a motion of host vehicle 4210 between two points. Based on known positions of front-facing camera 4212 and rear-facing camera 4414 relative to host vehicle 4210, positions of the cameras at the time images 4250 and 4350 are captured may be determined.

As shown in FIG. 43B, the reverse side of road sign 4202 has a significantly different appearance than the front side of road sign 4202 shown in FIG. 42B. For example, the post for road sign 4202 partially covers the sign and the printed information is not visible. Accordingly, using conventional techniques, it may be difficult to align point 4302 with point 4252 if they were included in separate sets of drive information (e.g., captured by two different vehicles driving in opposite directions). However, the information captured by host vehicle 4201 using images captured from front- and rear-facing cameras may be used to align drive information from opposing directions.

FIG. 44 illustrates example sets of drive information captured by vehicles traveling in opposite directions along road segment 4200, consistent with the disclosed embodiments. For example, a vehicle 4410 may traverse road segment 4200 in a first direction along lane 4222. Vehicle 4410 may be configured to capture drive information and transmit it to a remotely-located entity, such as server 4430. Vehicle 4410 may include a front-facing camera 4412 configured to capture images of the environment of vehicle 4410. Vehicle 4410 may identify one or more features associated with objects in road segment 4200 and may determine position descriptors associated with the identified feature points. For example, vehicle 4410 may identify a point 4414 associated with road sign 4202, and a point 4416 associated with crosswalk mark 4204. Vehicle 4410 may be configured to transmit drive information including points 4412 and 4416, and a trajectory 4402 to server 4430.

Vehicle 4420 may traverse road segment 4200 in a direction of travel opposite to vehicle 4410 along lane 4224, as shown in FIG. 44 . While both vehicles 4410 and 4420 are shown on road segment 4200 at the same time, it is to be understood that vehicles 4410 and 4420 may traverse road segment 4200 at different times. Similar to vehicle 4410, vehicle 4420 may be configured to capture drive information and transmit it to a server 4430. Vehicle 4420 may include a front-facing camera 4422 configured to capture images of the environment of vehicle 4420. Vehicle 4420 may identify one or more features associated with objects in road segment 4200 and may determine position descriptors associated with the identified feature points. For example, vehicle 4420 may identify a point 4424 associated with a back side of road sign 4202, a point 4416 associated with crosswalk mark 4204, and a point 4428 associated with tree 4206. Vehicle 4420 may be configured to transmit drive information including points 4412 and 4416, and a trajectory 4402 to server 4430. The points shown in FIG. 44 are provided by way of example, and not all of the points shown may be needed for alignment.

Server 4430 may be configured to generate a sparse map based on drive information collected by vehicles 4410 and 4420. Server 4430 may also transmit the sparse map (or data indicating updates to the sparse map) to one or more autonomous or semi-autonomous vehicles, which may be used for navigation. In some embodiments, server 4430 may correspond to sever 1230, as described above. Accordingly, any of the descriptions or disclosures made herein in reference to server 1230 may also apply to server 4430, and vice versa. Server 4430 may use drive information captured by host vehicle 4210 to align the drive information captured by vehicles 4410 and 4420. For example, server 4430 may receive points 4252, 4302, and/or 4306 (i.e., position descriptors), along with object type classifications, position information, or other information captured by host vehicle 4410. This captured information may be used to determine correlations between the drive information captured by vehicles 4410 and 4420.

The correlation may be determined in various ways. In some embodiments, the correlation may be based on detecting front sides of objects using front-facing camera 4212, and detecting the front sides of other objects using rear-facing camera. In other words, rear-facing camera 4214 can operate just like a forward facing camera of a car traveling in a direction opposite to host vehicle 4210. Accordingly, a set of drive information may be generated by host vehicle 4210 for each direction of travel along road segment 4200. Further, these two sets of drive information will be fully aligned because they were captured by the same vehicle along the same trajectory. When the front sides of these objects are detected by vehicles traveling in opposite directions, the drive information from the two vehicles can be aligned based on the drive information captured by host vehicle 4210.

As an illustrative example, host vehicle 4210 may identify feature point 4252 using front-facing camera 4212, as shown in FIG. 43B. Host vehicle 4210 may also identify feature point 4306 for tree 4206 using rear-facing camera 4214, as shown in FIG. 43B. Host vehicle 4210 may identify various other points along road segment 4200 using one or both of front-facing camera 4212 and rear-facing camera 4214. This drive information may be transmitted to server 4430 by host vehicle 4210. The feature points captured by front-facing camera 4212 may be fully aligned with the feature points captured using rear-facing camera 4214 as both sets of feature points were captured along the same trajectory. For example, server 4430 may determine a relative position of point 4252 and point 4306 based on location information received from host vehicle 4210 indicating the camera positions for front-facing camera 4212 and rear-facing camera 4214 when images 4250 and 4350 were captured, respectively. Based on this position information, a position of point 4252 may be defined relative to point 4306. In some embodiments, additional drive information using host vehicles with front- and rear-facing cameras may be acquired and used to further refine the relative positions of points 4252 and 4306 and other points associated with the front sides of objects.

Server 4430 may then receive drive information from vehicles 4410 and 4420, as described above. For example, server 4430 may receive drive information from vehicle 4410 including point 4414 and trajectory 4402. Server 4430 may further receive drive information from vehicle 4420 including point 4428 and trajectory 4404. Server 4430 may correlate point 4414 with point 4252 because both are associated with the front side of road sign 4202 and thus have a similar appearance in the image data. Similarly, server 4430 may correlate point 4428 with point 4306 because both are associated with the same side of tree 4206 and thus have a similar appearance in the image. Server 4430 may align the two sets of drive information based on points 4414 and 4428 and the known relationship between points 4252 and 4306 (determined as described above based on the drive information from host vehicle 4210. Accordingly, target trajectories 4402 and 4404 may be fully aligned within a sparse map generated by server 4430. While this alignment is described based on two sets of drive information for purposes of simplicity, it is to be understood that the same or similar processes may be performed across many sets of drive information captured by a plurality of vehicles in each direction. This example alignment process is described in further detail below with respect to processes 4500 and 4600.

In some embodiments, drive information from vehicles 4410 and 4420 may be aligned through various other techniques using drive information from host vehicle 4210. As another example, server 4430 may be configured to correlate points associated with the same object from different directions. For example, host vehicle 4210 may identify point 4252 associated with a front side of road sign 4202 using front-facing camera 4212. Host vehicle 4210 may also identify point 4302 associated with a back side of road sign 4202, as described above. Server 4430 (or host vehicle 4210) may associated both points 4252 and 4302 with the same object. For example, server 4430 may receive position information indicating the camera positions for front-facing camera 4212 and rear-facing camera 4214 when images 4250 and 4350 were captured, respectively. Based on this position information, points 4252 and 4302 may be determined to be associated with front and rear sides of the same object, respectively.

Accordingly, additional points associated with the front side of road sign 4202 and the rear side of road sign 4202 from different directions may be aligned. For example, server 4430 may receive drive information from vehicle 4410 including trajectory 4402 and point 4414 associated with a front side of road sign 4202, as described above. Server 4430 may further receive drive information from vehicle 4420 including trajectory 4404 and point 4424. Server 4430 may correlate point 4414 with point 4252 based on the similar appearance of road sign 4202 in image 4250 and images captured by vehicle 4410. Similarly, server 4430 may correlate point 4424 with point 4302 based on the similar appearance of the back of road sign 4202 in image 4350 and images captured by vehicle 4420. Server 4430 may correlate point 4414 with point 4424 based on the relationship between points 4252 and 4302 determined by host vehicle 4210. In other words, server 4430 may recognize that points 4414 and 4424 represent different sides of the same object based on the drive information provided by host vehicle 4210. Accordingly, target trajectories 4402 and 4404 may be fully aligned within a sparse map generated by server 4430. This example alignment process is described in further detail below with respect to processes 4700 and 4800.

FIG. 45 is a flowchart showing an example process 4500 for harvesting data for a sparse map, consistent with the disclosed embodiments. Process 4500 may be performed by at least one processing device of a host vehicle, such as processing unit 110, as described above. In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 4500. Further, process 4500 is not necessarily limited to the steps shown in FIG. 45 , and any steps or processes of the various embodiments described throughout the present disclosure may also be included in process 4500, including those described above with respect to FIGS. 42A, 42B, 43A, 43B, and 44 .

In step 4510, process 4500 may include receiving a first image captured by a forward-facing camera onboard a host vehicle as the host vehicle travels along a road segment in a first direction. The first image may be representative of an environment forward of the host vehicle. For example, step 4510 may include receiving image 4250 captured by host vehicle 4210 traveling along road segment 4200 in lane 4222. The first image may be captured by a forward-facing camera, such as front-facing camera 4212.

In step 4520, process 4500 may include receiving a second image captured by a rearward-facing camera onboard the host vehicle as the host vehicle travels along the road segment in the first direction. The second image may be representative of an environment behind the host vehicle. For example, step 4520 may include receiving image 4350 captured by host vehicle using rear-facing camera 4214, as shown in FIGS. 43A and 43B.

In step 4530, process 4500 may include detecting a first semantic feature represented in the first image. For example, step 4530 may include detecting road sign 4202 in image 4250. In some embodiments, the first semantic feature may be associated with a predetermined object type classification. For example, the object type classification may be a value indicating road sign 4202 as a road sign, a speed limit sign, a 30 MPH speed limit sign, or various other classifiers. The object type classification may be a numerical code, an alphanumerical code, a semantic description, or any other suitable value. While road sign 4204 is used by way of example, it is understood that the semantic feature may include various other features, such as a speed limit sign, a yield sign, a pole, a painted directional arrow, a traffic light, a billboard, or a building.

In step 4540, process 4500 may include identifying at least one position descriptor associated with the first semantic feature represented in the first image captured by the forward-facing camera. For example, step 4540 may include identifying a position descriptor associated with point 4252, as described above. In some embodiments, the at least one position descriptor associated with the first semantic feature may include an x-y image position relative to the first image. For example, the position descriptor may be a 2D point as described above with respect to FIG. 27 . In some embodiments, the at least one position descriptor associated with the first semantic feature may include an X-Y-Z position relative to a predetermined origin. For example, the position descriptor may be a 3D point as described above with respect to FIG. 28 . In some embodiments, the X-Y-Z position may be determined based on tracking a change in image position of the first semantic feature between the first image and at least one additional image and based on an output of at least one ego motion sensor associated with the host vehicle. For example, the at least one ego motion sensor may include at least one of a speedometer, an accelerometer, or a GPS receiver.

In step 4550, process 4500 may include detecting a second semantic feature represented in the second image. For example, step 4550 may include detecting tree 4206 in image 4350. Similar to the first semantic feature, the second semantic feature may be associated with a predetermined object type classification. For example, the object type classification may be a value indicating tree 4206 is a foliage object, a tree, a pine-tree, or various other classifiers. While tree 4206 is used by way of example, it is understood that the semantic feature may include various other features, such as a speed limit sign, a yield sign, a pole, a painted directional arrow, a traffic light, a billboard, or a building.

In step 4560, process 4500 may include identifying at least one position descriptor associated with the second semantic feature represented in the second image captured by the rearward-facing camera. For example, step 4560 may include identifying a position descriptor associated with point 4306, as described above. In some embodiments, the at least one position descriptor associated with the second semantic feature may include an x-y image position relative to the first image. For example, the position descriptor may be a 2D point as described above with respect to FIG. 27 . In some embodiments, the at least one position descriptor associated with the second semantic feature may include an X-Y-Z position relative to a predetermined origin. For example, the position descriptor may be a 3D point as described above with respect to FIG. 28 . In some embodiments, the X-Y-Z position may be determined based on tracking a change in image position of the first semantic feature between the second image and at least one additional image and based on an output of at least one ego motion sensor associated with the host vehicle. For example, the at least one ego motion sensor may include at least one of a speedometer, an accelerometer, or a GPS receiver.

In step 4570, process 4500 may include receiving position information indicative of a position of the forward-facing camera when the first image was captured and indicative of a position of the rearward-facing camera when the second image was captured. These camera positions may be based on an output of one or more position sensors, such as position sensor 2824 described above. For example, the position information may include at least one indicator of position determined based on an output of a GPS sensor associated with the host vehicle, based on an output of at least one ego motion sensor associated with the host vehicle, or based on a combination of an output of a GPS sensor associated with the host vehicle and an output of at least one ego motion sensor associated with the host vehicle.

In step 4580, process 4500 may include causing transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle. For example, step 4580 may include causing transmission of the drive information to server 4430 using wireless transceiver 172, as described above. The drive information may include various information collected by the host vehicle. In some embodiments, the drive information may include the at least one position descriptor associated with the first semantic feature, the at least one position descriptor associated with the second semantic feature, and the position information. In some embodiments, the drive information may further include one or more descriptors associated with each of the first and second semantic features. For example, the one or more descriptors may include a height or a width, a bounding box, a type classification, or various other information, as described above.

In some embodiments, the remotely-located entity may be configured to receive drive information from a plurality of other vehicles and correlate the received drive information based on the drive information transmitted by the host vehicle. For example, the remotely-located entity includes one or more processors configured to receive, in addition to the drive information transmitted by the host vehicle, drive information from a first plurality of other vehicles that travel along the road segment in the first direction along with drive information from a second plurality of other vehicles that travel along the road segment in a second direction opposite to the first dimension. For example, this may include drive information received by vehicles 4410 and 4420, as described above. The one or more processors may further be configured to correlate one or more aspects of the drive information received from the first plurality of other vehicles with one or more aspects of the drive information received from the second plurality of other vehicles based, at least in part, upon the first and second semantic features, as described above. In some embodiments, the correlation is further based on the position information.

FIG. 46 is a flowchart showing an example process 4600 for creating maps used in navigating autonomous or partially autonomous vehicles, consistent with the disclosed embodiments. Process 4600 may be performed by at least one processing device of a remotely located entity, such as server 4430, as described above. In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 4600. Further, process 4600 is not necessarily limited to the steps shown in FIG. 46 , and any steps or processes of the various embodiments described throughout the present disclosure may also be included in process 4600, including those described above with respect to FIGS. 42A, 42B, 43A, 43B, and 44 .

In step 4610, process 4600 may include receiving first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction. For example, step 4610 may include receiving drive information for road segment 4200 transmitted by vehicle 4410, as described above. Accordingly, the first plurality of vehicles may be traveling along road segment 4200 in lane 4222.

In step 4620, process 4600 may include receiving second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction. For example, step 4620 may include receiving drive information for road segment 4200 transmitted by vehicle 4420, as described above. Accordingly, the first plurality of vehicles may be traveling along road segment 4200 in lane 4224.

In step 4630, process 4600 may include receiving, from at least one vehicle equipped with a forward-facing camera and a rearward-facing camera, third drive information for the road segment. For example, step 4630 may include receiving drive information from host vehicle 4210 captured while traveling along road segment 4200. In some embodiments, the third drive information may be collected and transmitted according to at least some aspects of process 4400 described above. In some embodiments, the third drive information may include a position descriptor associated with a first semantic feature detected based on analysis of a forward-facing image captured by the forward-facing camera; and a position descriptor associated with a second semantic feature detected based on analysis of a rearward-facing image captured by the rearward-facing camera. In some embodiments, the position descriptor associated with the first semantic feature detected based on analysis of the forward-facing image, the second semantic feature detected based on analysis of the rearward-facing image, or both, may include an x-y position relative to the forward-facing image. For example, one or both of the position descriptors may be defined as a 2D point as described above with respect to FIG. 27 . In some embodiments, the position descriptor associated with the first semantic feature detected based on analysis of the forward-facing image, the second semantic feature detected based on analysis of the rearward-facing image, or both, may include an X-Y-Z position relative to a predetermined origin. For example, one or both of the position descriptors may be defined as a 3D point as described above with respect to FIG. 28 .

In step 4640, process 4600 may include correlating one or more aspects of the first drive information and the second drive information based, at least in part, upon the position descriptor associated with the first semantic feature detected based on analysis of the forward-facing image and upon the position descriptor associated with the second semantic feature detected based on analysis of the rearward-facing image captured by the rearward-facing camera. For example, the position descriptor associated with the first semantic feature may include point 4252, and the position descriptor associated with the second semantic feature may include point 4306. Step 4640 may include correlating positions of point 4416 (which may be included in the first drive information) and point 4428 (which may be included in the second drive information) based on a relationship between points 4252 and 4306 determined based on the third drive information, as described above.

In some embodiments, the third drive information may further include an indicator of a position of the forward-facing camera when the forward-facing image was captured, and an indicator of a position of the rearward-facing camera when the rearward-facing image was captured. As described above, the correlation of the one or more aspects of the first drive information and the second drive information may further be based on the indicator of a position of the forward-facing camera when the forward-facing image was captured and based on the indicator of a position of the rearward-facing camera when the rearward-facing image was captured. For example, this may include an indicator of the position of front-facing camera 4212 when image 4250 was taken and an indicator of the position of rear-facing camera 4214 when image 4350 was taken. In some embodiments, these camera positions may be based on an output of one or more position sensors associated with the at least one vehicle equipped with a forward-facing camera and a rearward-facing camera, such as position sensor 2824 described above. For example, the indicator of a position of the forward-facing camera position and the indicator of a position of the rearward-facing camera may be determined based on an output of a GPS sensor, an output of at least one ego motion sensor, or a combination of an output of a GPS sensor and an output of at least one ego motion sensor, both the GPS sensor and the at least one ego motion sensor being associated with the at least one vehicle equipped with a forward-facing camera and a rearward-facing camera.

In step 4650, process 4600 may include generating the sparse map based, at least in part, on the correlation of the first drive information and the second drive information. The sparse map may include at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction. For example, the sparse map may include target trajectories 4402 and 4404, as described above.

FIG. 47 is a flowchart showing an example process 4700 for harvesting data for a sparse map, consistent with the disclosed embodiments. Process 4700 may be performed by at least one processing device of a host vehicle, such as processing unit 110, as described above. In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 4700. Further, process 4700 is not necessarily limited to the steps shown in FIG. 47 , and any steps or processes of the various embodiments described throughout the present disclosure may also be included in process 4700, including those described above with respect to FIGS. 42A, 42B, 43A, 43B, and 44 .

In step 4710, process 4700 may include receiving a first image captured by a forward-facing camera onboard a host vehicle as the host vehicle travels along a road segment in a first direction. The first image may be representative of an environment forward of the host vehicle. For example, step 4710 may include receiving image 4250 captured by host vehicle 4210 traveling along road segment 4200 in lane 4222. The first image may be captured by a forward-facing camera, such as front-facing camera 4212.

In step 4720, process 4700 may include receiving a second image captured by a rearward-facing camera onboard the host vehicle as the host vehicle travels along the road segment in the first direction. The second image may be representative of an environment behind the host vehicle. For example, step 4720 may include receiving image 4350 captured by host vehicle using rear-facing camera 4214, as shown in FIGS. 43A and 43B.

In step 4730, process 4700 may include detecting at least one object represented in the first image. For example, step 4730 may include detecting road sign 4202 in image 4250.

In step 4740, process 4700 may include identify at least one front side two-dimensional feature point. The front side two-dimensional feature point may be associated with the at least one object represented in the first image. For example, step 4740 may include detecting point 4252, as described above. In some embodiments, the at least one front side two-dimensional feature point may include an x-y position relative to the first image. For example, the front side two-dimensional feature point may be a 2D point, as described above with respect to FIG. 27 .

In step 4750, process 4700 may include detecting a representation of the at least one object in the second image. For example, step 4750 may include detecting the back side of road sign 4202 in image 4350. In some embodiments, the representation of the at least one object in the second image may be detected based on a predetermined relationship between the forward-facing camera and the rearward-facing camera. For example, the positions of front-facing camera 4212 and rear-facing camera 4214 may be defined relative to host vehicle 4210. In some embodiments, the representation of the at least one object in the second image may further be detected based on an output of at least one ego motion sensor associated with the host vehicle. For example, step 4750 may include tracking a motion of host vehicle 4210 from a location where image 4250 was captured and detecting the back side of road sign 4202 image 4350 once host vehicle 4210 reaches a position where the back side of road sign 4202 is expected to be visible. Host vehicle 4210 may detect the back side of road sign 4202 based on an expected position of road sign 4202 within image 4350. For example, based on the relationship between front-facing camera 4212 and rear-facing camera 4214 and the ego motion of host vehicle 4210, an expected position of the back side of road sign 4202 may be determined, which may be used to identify the back side of road sign 4202 in image 4350.

In step 4760, process 4700 may include identifying at least one rear side two-dimensional feature point. The at least one rear side two-dimensional feature point may be associated with the at least one object represented in the second image. For example, step 4760 may include detecting point 4302, as described above. In some embodiments, the at least one rear side two-dimensional feature point may include an x-y position relative to the first image. For example, the front side two-dimensional feature point may be a 2D point, as described above with respect to FIG. 27 .

In step 4770, process 4700 may include receiving position information indicative of a position of the forward-facing camera when the first image was captured and indicative of a position of the rearward-facing camera when the second image was captured. These camera positions may be based on an output of one or more position sensors, such as position sensor 2824 described above. For example, the position information may include at least one indicator of position determined based on an output of a GPS sensor associated with the host vehicle, based on an output of at least one ego motion sensor associated with the host vehicle, or based on a combination of an output of a GPS sensor associated with the host vehicle and an output of at least one ego motion sensor associated with the host vehicle.

In step 4780, process 4700 may include causing transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle. For example, step 4770 may include causing transmission of the drive information to server 4430 using wireless transceiver 172, as described above. The drive information may include various information collected by the host vehicle. In some embodiments, the drive information may include the at least one front side two-dimensional feature point, the at least one rear side two-dimensional feature point, and the position information. In some embodiments, the drive information may further include an indicator that the at least one front side two-dimensional feature point and the at least one rear side two-dimensional feature point are associated with the same object. For example, this may include a reference or other information linking the front side and rear side two-dimensional feature points.

In some embodiments, the remotely-located entity may be configured to receive drive information from a plurality of other vehicles and correlate the received drive information based on the drive information transmitted by the host vehicle. For example, the remotely-located entity includes one or more processors configured to receive, in addition to the drive information transmitted by the host vehicle, drive information from a first plurality of other vehicles that travel along the road segment in the first direction along with drive information from a second plurality of other vehicles that travel along the road segment in a second direction opposite to the first dimension. For example, this may include drive information received by vehicles 4410 and 4420, as described above. The one or more processors may further be configured to correlate one or more aspects of the drive information received from the first plurality of other vehicles with one or more aspects of the drive information received from the second plurality of other vehicles based, at least in part, upon the first and second semantic features, as described above. In some embodiments, the correlation is further based on the position information. In some embodiments, the correlation may further be based on an indicator received as part of the drive information received from the host vehicle that the at least one front side two-dimensional feature point and the at least one rear side two-dimensional feature point are associated with the same object.

FIG. 48 is a flowchart showing an example process 4800 for creating maps used in navigating autonomous or partially autonomous vehicles, consistent with the disclosed embodiments. Process 4800 may be performed by at least one processing device of a remotely located entity, such as server 4430, as described above. In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process 4800. Further, process 4800 is not necessarily limited to the steps shown in FIG. 48 , and any steps or processes of the various embodiments described throughout the present disclosure may also be included in process 4800, including those described above with respect to FIGS. 42A, 42B, 43A, 43B, and 44 .

In step 4810, process 4800 may include receiving first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction. For example, step 4810 may include receiving drive information for road segment 4200 transmitted by vehicle 4410, as described above. Accordingly, the first plurality of vehicles may be traveling along road segment 4200 in lane 4222.

In step 4820, process 4800 may include receiving second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction. For example, step 4810 may include receiving drive information for road segment 4200 transmitted by vehicle 4420, as described above. Accordingly, the first plurality of vehicles may be traveling along road segment 4200 in lane 4224.

In step 4830, process 4800 may include receiving, from at least one vehicle equipped with a forward-facing camera and a rearward-facing camera, third drive information for the road segment. For example, step 4810 may include receiving drive information from host vehicle 4210 captured while traveling along road segment 4200. In some embodiments, the third drive information may be collected and transmitted according to at least some aspects of process 4400 described above. In some embodiments, the third drive information may include at least one front side two-dimensional feature point generated based on analysis of a representation of an object in a forward-facing image captured by the forward-facing camera. For example, the third drive information may include point 4252 generated based on analysis of a front of road sign 4202 in image 4250. In some embodiments, the at least one front side two-dimensional feature point may include an x-y position relative to the first image. For example, the front side two-dimensional feature point may be a 2D point, as described above with respect to FIG. 27 .

The third drive information may further include at least one rear side two-dimensional feature point generated based on analysis of a representation of the object in a rearward-facing image captured by the rearward-facing camera. For example, the third drive information may include point 4302 generated based on analysis of a back side of road sign 4202 in image 4350. In some embodiments, the at least one rear side two-dimensional feature point may include an x-y position relative to the second image. For example, the rear side two-dimensional feature point may be a 2D point, as described above with respect to FIG. 27 .

In step 4840, process 4800 may include correlating one or more aspects of the first drive information and the second drive information based, at least in part, upon the at least one front side two-dimensional feature point and upon the at least one rear side two-dimensional feature point included in the third drive information. For example, the first drive information may include a point 4414, and the second drive information may include point 4424. Step 4840 may include correlating positions of point 4414 and point 4424 based on a relationship between points 4252 and 4302 determined based on the third drive information, as described above.

In some embodiments, the third drive information may further include an indicator of a position of the forward-facing camera when the forward-facing image was captured, and an indicator of a position of the rearward-facing camera when the rearward-facing image was captured. As described above, the correlation of the one or more aspects of the first drive information and the second drive information may further be based on the indicator of a position of the forward-facing camera when the forward-facing image was captured and based on the indicator of a position of the rearward-facing camera when the rearward-facing image was captured. For example, this may include an indicator of the position of front-facing camera 4212 when image 4250 was taken and an indicator of the position of rear-facing camera 4214 when image 4350 was taken. In some embodiments, these camera positions may be based on an output of one or more position sensors associated with the at least one vehicle equipped with a forward-facing camera and a rearward-facing camera, such as position sensor 2824 described above. For example, the indicator of a position of the forward-facing camera position and the indicator of a position of the rearward-facing camera may be determined based on an output of a GPS sensor, an output of at least one ego motion sensor, or a combination of an output of a GPS sensor and an output of at least one ego motion sensor, both the GPS sensor and the at least one ego motion sensor being associated with the at least one vehicle equipped with a forward-facing camera and a rearward-facing camera. In some embodiments, the third drive information further includes an indicator that the at least one front side two-dimensional feature point and the at least one rear side two-dimensional feature point are associated with the same object. The correlation of the one or more aspects of the first drive information and the second drive information may further be based on the indicator that the at least one front side two-dimensional feature point and the at least one rear side two-dimensional feature point are associated with the same object.

In step 4850, process 4800 may include generating the sparse map based, at least in part, on the correlation of the first drive information and the second drive information. The sparse map may include at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction. For example, the sparse map may include target trajectories 4402 and 4404, as described above.

Bandwidth Management for Map Generation and Refinement

As described elsewhere in this disclosure, harnessing and interpreting vast volumes of data (e.g., captured image data, map data, GPS data, sensor data, etc.) collected by vehicles poses a multitude of design challenges. For example, the data collected by a vehicle may need to be uploaded to a server. The sheer quantity of data to be uploaded may easily cripple or hinder the transmission bandwidth of the vehicle. Additionally, analyzing new data by a server to update the relevant portion of a map based on the new data may also be challenging. Moreover, different density levels may be used for mapping different types of features. For example, a density level of 330 kB per km may be needed for mapping non-semantic features, compared with about a density level of 20 kB per km for semantic features. Given the computation resources required for collecting non-semantic feature information and certain hard limit bandwidth caps (e.g., 100 MB per year) that may be imposed to each of the vehicles, there may be insufficient resources available to the vehicles to collect non-semantic feature information all the time.

The systems and methods may enable controlling not only whether drive data is collected, but also when and how drive data is collected. For example, the disclosed systems and methods may enable a server to determine whether a host vehicle is entering a zone including a region of interest. Upon confirmation that the vehicle has entered the zone, the server can cause the vehicle to begin collecting higher density non-semantic features information. If the vehicle is determined to have driven through the point of interest, then the server can cause the vehicle to upload the collected non-semantic features information. If the vehicle is determined not to have driven through the region of interest, the server may cause the host vehicle to discard the collected non-semantic features information. The disclosed systems and methods may also enable the server to update a map based on the non-semantic features information collected by the vehicle.

FIG. 49 illustrates an exemplary system for automatically generating a navigational map relative to one or more road segments, consistent with the disclosed embodiments. As illustrated in FIG. 49 , system 4900 may include a server 4901, one or more vehicles 4902, and one or more vehicle devices 4903 associated with a vehicle, a database 4904, and a network 4905. For example, vehicle 4902 and/or vehicle device 4903 may be configured to collect, at a first density level, first navigational information associated with the environment traversed by vehicle 4902 when vehicle 4902 travels outside of a predetermined distance from a geographical region of interest. Vehicle 4902 and/or vehicle device 4903 may also be configured to collect, at a second density level that may be greater than the first density level, second navigational information associated with the environment traversed by vehicle 4902 when vehicle 4902 travels at or within the predetermined distance from the geographical region of interest.

Server 4901 may be configured to receive the first and/or second navigational information associated with the environment traversed by vehicle 4902. Database 4904 may be configured to store information for the components of system 4900 (e.g., server 4901, vehicle 4902, and/or vehicle device 4903). Network 4905 may be configured to facilitate communications among the components of system 4900.

Server 4901 may be configured to cause collection of first navigational information associated with an environment traversed by vehicle 4902. The first navigational information may be collected at a first density level. Server 4901 may also be configured to determine the location of vehicle 4902 based on output associated with a GPS sensor associated with vehicle 4902. Server 4901 may further be configured to determine whether vehicle 4902 is at or within a predetermined distance from the geographical region of interest (or a boundary thereof). Server 4901 may also be configured to cause collection of second navigational information associated with the environment traversed by vehicle 4902, based on the determination that the location of vehicle 4902 is at or within the predetermined distance from the geographical region of interest. The second navigational information may be collected at a second density level that may greater than the first density level. Server 4901 may further be configured to cause vehicle 4902 to upload at least one of the collected first navigational information or the collected second navigational information (or a portion thereof) from vehicle 4902. Server 4901 may also be configured to update a navigational map based on the uploaded at least one of the collected first navigational information or the collected second navigational information. In some embodiments, server 4901 may be a cloud server that performs the functions disclosed herein. The term “cloud server” refers to a computer platform that provides services via a network, such as the Internet. In this example configuration, server 4901 may use virtual machines that may not correspond to individual hardware. For example, computational and/or storage capabilities may be implemented by allocating appropriate portions of desirable computation/storage power from a scalable repository, such as a data center or a distributed computing environment. In one example, server 4901 may implement the methods described herein using customized hard-wired logic, one or more Application Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs), firmware, and/or program logic which, in combination with the computer system, cause server 4901 to be a special-purpose machine.

Vehicle 4902 and/or vehicle device 4903 may be configured to collect first navigational information when it travels beyond the predetermined distance from the geographical region of interest (or a boundary thereof). Vehicle 4902 and/or vehicle device 4903 may also be configured to collect second navigational information when it travels at or within the predetermined distance from the geographical region of interest (or a boundary thereof). Vehicle 4902 and/or vehicle device 4903 may further be configured to upload at least one the collected first navigational information and the collected second navigational information (or a portion thereof). In some embodiments, vehicle 4902 may receive map information (e.g., an updated map) from server 4901. Map information may include data relating to the position in a reference coordinate system of various items, including, for example, roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, a sparse data model including polynomial representations of certain road features (e.g., lane markings), target trajectories for the host vehicle, or the like, or a combination thereof. In some embodiments, vehicle 4902 and/or vehicle device 4903 may also be configured to plan a routing path and/or navigate vehicle 4902 according to the map information. For example, vehicle 4902 and/or vehicle device 4903 may be configured to determine a route to a destination based on the map information. Alternatively or additionally, vehicle 4902 and/or vehicle device 4903 may be configured to perform at least one navigational action (e.g., making a turn, stopping at a location, etc.) based on the received map information. In some embodiments, vehicle 4902 may include a device having a similar configuration and/or performing similar functions as system 100 described above. Alternatively or additionally, vehicle device 4903 may have a similar configuration and/or performing similar functions as system 100 described above.

Database 4904 may include a map database configured to store map information for the components of system 4900 (e.g., server 4901, vehicle 4902, and/or vehicle device 4903). In some embodiments, server 4901, vehicle 4902, and/or vehicle device 4903 may be configured to access database 4904, and obtain data stored from and/or upload data to database 4904 via network 4905. For example, server 4901 may transmit data relating to map information to database 4904 for storage. Vehicle 4902 and/or vehicle device 4903 may download map information and/or data from database 4904. In some embodiments, database 4904 may include data relating to the position, in a reference coordinate system, of various items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, or the like, or a combination thereof. In some embodiments, database 4904 may include a database similar to map database 160 described elsewhere in this disclosure.

Network 4905 may be any type of network (including infrastructure) that provides communications, exchanges information, and/or facilitates the exchange of information between the components of system 4900. For example, network 4905 may include or be part of the Internet, a Local Area Network, wireless network (e.g., a Wi-Fi/302.11 network), or other suitable connections. In other embodiments, one or more components of system 4900 may communicate directly through dedicated communication links, such as, for example, a telephone network, an extranet, an intranet, the Internet, satellite communications, off-line communications, wireless communications, transponder communications, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), and so forth.

FIGS. 50A, 50B, and 50C illustrate an exemplary process for collecting navigational information, consistent with disclosed embodiments. As illustrated in FIG. 50A, vehicle 4902 may travel in a region 5000. Server 4901 may be configured to determine the location of vehicle 4902 based on, for example, GPS data received from a GPS device associated with vehicle 4902. Server 4901 may also be configured to determine that vehicle 4902 is beyond a predetermined distance d from geographical region of interest 5001. Server 4901 may further be configured to cause vehicle 4902 to collect, at a first density level, first navigational information associated with the environment traversed by vehicle 4902. Vehicle 4902 may move in region 5000 as illustrated in FIG. 50B. Server 4901 may be configured to determine that vehicle 4902 is within the predetermined distance d from geographical region of interest 5001 based on the location of vehicle 4902. Server 4901 may also be configured to cause vehicle 4902 to collect, at a second density level that may be greater than the first density level, second navigational information associated with the environment traversed by vehicle 4902. Vehicle 4902 may move in region 5000 as illustrated in FIG. 50C.

A greater density level may indicate a greater amount of collected data per a unit length of a road segment. For example, the second navigational information collected at the second density level may include a denser set of similar objects (e.g., more landmarks) than the first navigational information collected at the first density level. In some embodiments, the density level may correspond to a number of three-dimensional points that are captured per meter. For example, as described in further detail above, a host vehicle may capture three-dimensional points associated with features in the environment of the vehicle, which may be used to align drive information from different directions. The disclosed systems and methods may be used to define different densities of three-dimensional points to be collected. Various other aspects associated with three-dimensional point collection may be defined, such as controlling whether or not the three-dimensional points are collected, defining a type of features to be associated with three-dimensional points (e.g., semantic versus non-semantic, etc.), or various other aspects.

Alternatively or additionally, the second navigational information collected at the second density level may include more categories of data collected than the first navigational information collected at the first density level. For example, the second navigational information collected at the second density level may include more non-sematic features (e.g., descriptors), which may need more data compared to that for sematic features, than the first navigational information has. In some embodiments, the second navigational information may include one or more non-sematic features, while the first navigational information may not include any non-sematic features. Examples of semantic features and non-semantic features were discussed earlier in this disclosure. Alternatively or additionally, the second navigational information collected at the second density level may include sensor data from more and/or different sensors than the first navigational information collected at the first density level. For example, the first navigational information may include GPS data received from a GPS sensor. The second navigational information may include GPS data and LIDAR data received from a LIDAR system. As another example, the first navigational information may include GPS data and LIDAR data, and the second navigational information may include GPS data and image (and/or VIDAR) data received from a VIDAR system. As still another example, the first navigational information may include one or more images captured by a camera installed on the vehicle, and the second navigational information may include one or more images captured by the camera and LIDAR data received from a LIDAR system. As still another example, the first navigational information may include the data received from an accelerometer sensor and data received from a LIDAR system, and the data received from the accelerometer sensor and image (and/or VIDAR) data received from a VIDAR system.

In some embodiments, a landmark may include a combination of objects (or landmarks) as long as the combination of objects is distinguishable (it can be distinguished from other nearby sets of objects). For example, landmarks of the same type, including a set of landmarks that may constitute a combination landmark (a landmark that may be constituted by a combination of semantic objects) may distanced apart from one another by a certain distance (e.g., the distance that corresponds (or is equal) to the minimal resolution of the GPS device associated with a vehicle.

Server 4901 may be configured to determine that vehicle 4902 is within geographical region of interest 5001. Vehicle 4902 may be configured to continue to collect second navigational information at the second density level when it travels within geographical region of interest 5001. Server 4901 may also be configured to cause vehicle 4902 to upload at least one of the first navigational information and the second navigational information to server 4901.

In some embodiments, one or more attributes of the drive may be assessed to determine whether the first and/or second navigational information should be uploaded. For example, a system may identify a need for data associated with a particular road segment to be collected. For example, it may be difficult to determine whether a vehicle will travel along the road segment until after the vehicle passes through a region surrounding the road segment. Accordingly, an area of interest surrounding the road segment may be defined such that a configuration of how data is collected is changed. For example, as vehicle 4902 enters a region 5001 surrounding the road segment, it may be triggered to collect second navigation information at the second density level, as described above. In some embodiments, the large amount of data collected in the second navigation information may be preferred only if the vehicle traverses a particular road segment of interest. Accordingly, the second navigation information may be discarded if vehicle 4902 does not traverse the road segment of interest. Vehicle 4902 may transmit location information, such as GPS data to server 4901. In response, server 4901 may analyze the GPS data to determine whether vehicle 4902 entered the road segment (or another region of interest). If so, server 4901 may transmit instructions to vehicle 4902 to upload the full (or partial) set of navigation information.

In some embodiments, one or more rules may be implemented to define how much data should be collected and/or saved. For example, this may include geographic rules as described above. As another example, a rule may define a frequency at which data is uploaded to a server. In some embodiments, a frequency rule may control a rate at which vehicles upload data to a server, which may allow data to be spread across the day rather than when a vehicle traverses a particular road segment. In some embodiments, the rule may define a rate at which vehicles collect and upload data associated with a particular road segment. For example, a frequency rule may define a period of 34 minutes. If a server identifies a drive in the road segment at 11:00 AM, drives by other vehicles through the same road segment will not collect and/or upload data until 11:34 AM. Accordingly, the frequency at which data is received may be limited. In some embodiments, a server may accept data for an entire drive by a vehicle, even if data from other road segments within the drive is not required by an associated frequency rule associated with those segments. For example, a host vehicle may upload data from a first segment according to a first frequency rule and may include data from a second road segments, even if a second frequency rule for the second road segment does not indicate data should be uploaded.

FIG. 51 is a flowchart showing an exemplary process for automatically generating a navigational map relative to one or more road segments, consistent with the disclosed embodiments. One or more steps of process 5100 may be performed by a vehicle (e.g., vehicle 4902), a device associated with the host vehicle (e.g., vehicle device 4903), and/or a server (e.g., server 4901). While the descriptions of process 5100 provided below use server 4901 as an example, one skilled in the art would appreciate that one or more steps of process 5100 may be performed by a vehicle (e.g., vehicle 4902) and a vehicle device configured to collect navigational information and transmit the navigational information to a server. In addition, in various steps described herein, a vehicle may upload certain information and/or data to a server, and one skilled in the art would understand that, alternatively or additionally, the vehicle may upload the information and/or data to a database (e.g., database 4904) via a network. Moreover, one skilled in the art would understand that although the descriptions of process 5100 provided below use vehicle 4902 as an example, process 5100 is not limited to one vehicle. For example, server 4901 may be configured to determine the locations of a plurality of vehicles and cause the plurality of vehicles to collect first and/or second navigational information. Server 4901 may also be configured to cause the plurality of vehicles to upload the collected first and/or second navigational information, and update a navigational map based on the received first and/or second navigational information.

At step 5101, first navigational information associated with an environment traversed by a host vehicle may be collected. For example, server 4901 may be configured to cause collection of first navigational information, by vehicle 4902 at a first density level, associated with an environment traversed by vehicle 4902. By way of example, as illustrated in FIG. 50A, vehicle 4902 may travel in a region 5000. Server 4901 may be configured to cause vehicle 4902 to collect navigational information associated with vehicle 4902's environment by transmitting a request to vehicle 4902 to collect navigational information at the first level.

In some embodiments, vehicle 4902 may collect navigational information determined based on signals received from one or more sensors, such as a GPS device, a speed sensor, an accelerometer, a suspension sensor, a camera, and a LIDAR device, or the like, or a combination thereof. Exemplary navigational information may include information and/or data relating to the locations of the vehicle, speeds of the vehicle, driving directions of the vehicle, landmarks associated with one or more road segments, roads, one or more road features, one or more infrastructure objects, water features, geographic features, points of interest (e.g., office buildings, restaurants, gas stations, etc.), one or more images associated with one or more road segments, LIDAR data associated with one or more road segments, a sparse data model including polynomial representations of certain road features (e.g., lane markings), target trajectories for the host vehicle, or the like, or a combination thereof. For example, the first navigational information may include location information associated with one or more landmarks associated with the one or more road segments. Alternatively or additionally, the first navigational information may include location information associated with one or more road features associated with the one or more road segments.

In some embodiments, the first navigational information may be associated with a first density level. A density level may indicate certain categories of navigational information to collect (e.g., one or more types of navigational information collected), a collection rate of navigational information (e.g., how many data points per unit time, such as per second, per minute, per hour, etc.), an accuracy level of navigational information (e.g., a higher or lower data resolution), or the like, or a combination thereof. In some embodiments, a density level may be associated with a data density (e.g., less than 10 MB per kilometer of roads, less than 5 MB per kilometer of roads, 2 MB per kilometer of roads, less than 1 MB per kilometer of roads, less than 500 kB per kilometer of roads, less than 100 kB per kilometer of roads, less than 10 kB per kilometer of roads, or less than 2 kB per kilometer of roads).

In some embodiments, vehicle 4902 may collect first navigational information without input or a request from server 4901. For example, vehicle 4902 may operate in a “default” collection mode in which navigational information is collected at a predetermined density level (e.g., the first density level). Alternatively, vehicle 4902 may collect navigational information at a predetermined density level based on a setting associated with a condition of vehicle 4902 (e.g., a speed, heading direction, etc.). As yet another alternative, vehicle 4902 may collect navigational information based on a density level transmitted from server 4901 to vehicle 4902.

At step 5102, a location of the host vehicle may be determined based on output associated with one or more sensors of the host vehicle. For example, vehicle 4902 may be configured to receive output associated with a GPS sensor and transmit the output to server 4901. Server 4901 may be configured to determine the location of vehicle 4902 based on received output associated with the GPS sensor. Alternatively or additionally, vehicle 4902 may be configured to determine its location based on the received output associated with the GPS sensor and transmit the location of vehicle 4902 to server 4901. Exemplary sensors may include a speed sensor, an accelerometer, a camera, a LIDAR device, or the like, or a combination thereof. Alternatively or additionally, vehicle 4902 may use map information to determine a location.

In some embodiments, the location of the vehicle 4902 may be determined at predetermined time intervals. For example, the location of vehicle 4902 may be determined once every second or every minute. Alternatively or additionally, the location of vehicle 4902 may be determined based on the speed of the vehicle 4902. For example, the location of vehicle 4902 may be determined more frequently if it is determined (by vehicle 4902 and/or server 4901) that vehicle 4902 travels at a higher speed. Alternatively or additionally, the location of vehicle 4902 may be determined based on the region in which vehicle 4902 is. For example, the location of vehicle 4902 may be determined more frequently if it is determined (by vehicle 4902 and/or server 4901) that vehicle 4902 is in an urban area (or less frequently if vehicle 4902 is in a rural area).

At step 5103, whether the location of the host vehicle is at or within a predetermined distance from a geographical region of interest may be determined. For example, as illustrated in FIGS. 50A and 50B, server 4901 may be configured to determine a geographical region of interest 5001 and a predetermined distance d from geographical region of interest 5001 (or a boundary thereof). Server 4901 may also be configured to determine whether vehicle 4902 is at or within the predetermined distance d from geographical region of interest 5001 (or a boundary thereof). As illustrated in FIG. 50A, server 4901 may be configured to determine that vehicle 4902 is not at or within the predetermined distance d from geographical region of interest 5001 (or a boundary thereof). Alternatively, as illustrated in FIG. 50B, server 4901 may be configured to determine that vehicle 4902 is at or within the predetermined distance d from geographical region of interest 5001 (or a boundary thereof). Alternatively or additionally, vehicle 4902 may be configured to determine whether vehicle 4902 is at or within the predetermined distance d from geographical region of interest 5001 (or a boundary thereof). For example, server 4901 may transmit a geographical region of interest 5001 and a predetermined distance d from geographical region of interest 5001 (or a boundary thereof) to vehicle 4902. Vehicle 4902 may be configured to determine whether vehicle 4902 is at or within the predetermined distance d from geographical region of interest 5001 (or a boundary thereof) based on the location of the vehicle 4902 determined as described elsewhere in this disclosure. Vehicle 4902 may be configured to transmit the result of the determination of being at or within (or outside of) the predetermined distance d from geographical region of interest 5001 (or a boundary thereof) to server 4901.

In some embodiments, server 4901 may be configured to determine a geographical region of interest based on existing information of the geographical region of interest. For example, server 4901 may be configured to determine that the existing map data of a region may be inadequate (e.g., does not include certain details regarding a road feature associated with a road segment in the region). By way of example, server 4901 may be configured to determine a particular road segment (e.g., 1 km in length) that may need accurate alignment. Server 4901 may also be configured to determine a geographical region of interest (e.g., a square or circle that covers the road segment). As another example, server 4901 may determine to update map information of a region and may designate the region as a geographical region of interest.

A geographical region of interest may be a geographic region in a map or a coordinate system. A geographical region of interest may include one or more of road segments, streets, intersections, highways, highway junctions, landmarks, infrastructure objects, geographic features, points of interest (such as businesses, restaurants, gas stations, etc.), or the like, or a combination thereof. Geographical regions of interest may vary in shape and/or size. A geographical region of interest may include a triangular shape, a quadrilateral shape, a parallelogram shape, a rectangular shape, a square (or substantially square) shape, a trapezoid shape, a diamond shape, a hexagon shape, an octagon shape, a circular (or substantially circular) shape, an oval shape, an egg shape, or the like, or a combination thereof. One skilled in the art would understand that the shape of a geographical region of interest is not limited to the exemplary shapes described in this disclosure. Other shapes are also possible. For example, a geographical region of interest may include an irregular shape (e.g., determined based on one or more boundaries of jurisdictions, such as countries, states, counties, cities, and/or roads) and/or a portion of any of the shapes described herein. The size of a geographical region of interest may be in a range of 1 square meter to 100 square kilometers. In some embodiments, the size of a geographical region of interest may be restricted into a subrange of 1 square meter to 0.1 square kilometers, 0.1 to 0.25 square kilometers, 0.25 square kilometers to 1 square kilometer, 1 square kilometers to 10 square kilometers, and 10 to 25 square kilometers, 25 to square kilometers, and 50 to 100 square kilometers.

In some embodiments, the predetermined distance may include a direct distance, a route distance (e.g., a distance for the host vehicle to travel to the geographical region of interest), or the like, or a combination thereof. The predetermined distance may be in a range of 1 meter to 100 kilometers. In some embodiments, the predetermined distance may be restricted into subranges of 1 to 5 meters, 5 to 10 meters, 10 to 50 meters, 50 to 100 meters, 100 to 500 meters, 500 to 1000 meters, 1 to 5 kilometers, 5 to 10 kilometers, 10 to 50 kilometers, and 50 to 100 kilometers. Alternatively or additionally, the predetermined distance may include a distance indicator based on the separation of the vehicle from the geographical region of interest (or a boundary thereof) in terms of the number of street blocks, the number of exits on a highway, or the like, or a combination thereof. For example, a predetermined distance may include a distance indicator indicating three street blocks.

In some embodiments, server 4901 may be configured to determine an approximation of a geographical region based on the geographical region of interest. For example, server 4901 may extend the geographical region of interest by adding the predetermined distance to the boundaries of the geographical region of interest to obtain a rough geographical region. Server 4901 (and/or vehicle 4902) may also be configured to determine whether vehicle 4902 is at or within the rough geographical region based on the location of vehicle 4902.

In some embodiments, server 4901 may transmit information relating to the geographical region of interest and/or predetermined distance to vehicle 4902. For example, server 4901 may transmit the boundaries of the geographical region of interest and/or the predetermined distance to vehicle 4902.

At step 5104, in response to the determination that the location of the host vehicle is at or within the predetermined distance from the geographical region of interest, second navigational information associated with the environment traversed by the host vehicle may be collected. For example, server 4901 may be configured to cause collection of second navigational information associated with the environment traversed by vehicle 4902, based on the determination that the location of vehicle 4902 is at or within the predetermined distance from the geographical region of interest.

For example, as illustrated in FIG. 50B, server 4901 may be configured to determine that vehicle 4902 is within geographical region of interest 5001. Server 4901 may also be configured to transmit an instruction to vehicle 4902 to collect second navigational information, and vehicle 4902 may collect second navigational information in response to the received instruction.

In some embodiments, the second navigational information may include more categories of information than the first navigational information. For example, the first navigational information may include landmarks and road features associated with one or more road segments (i.e., two categories of information). The second navigational information may include not only landmarks and road features associated with one or more road segments, but also one or more infrastructure objects, water features, geographic features, and/or points of interest associated with one or more road segments.

In some embodiments, the second navigational information may be associated with a second density level that is higher than the first density level. For example, the second density level may be at least two times greater than the first density level. By way of example, the first navigational information may include cloud point data measured at a first density level (e.g., 300 points every 15 meters) by a LIDAR device associated with vehicle 4902. The second navigational information may include cloud point data measured by the LIDAR device at a second density level (e.g., 800 points every 15 meters), which is more than two times greater than the first density level. As another example, the second density level may be at least five times greater than the first density level. As another example, the second density level may be at least ten times greater than the first density level.

Alternatively or additionally, the second navigational information may be collected at a collection rate higher than a collection rate at which the first navigational information is collected. By way of example, the first navigational information may include images of the environment of vehicle 4902 captured by a camera onboard vehicle 4902 at a first image collection rate. The second navigational information may include images of the environment of vehicle 4902 captured by the camera onboard vehicle 4902 at a second image collection rate that is higher than the first image collection rate. An image collection rate may include a collection rate based on time and/or distance that vehicle 4902 travels. For example, a first image collection rate may be 10 frames per second (FPS), and a second image collection rate may be 30 or 60 FPS. As another example, a first image collection rate may be 1 frame per meter that vehicle 4902 travels, and a second image collection rate may be 5 or 10 frames per meter that vehicle 4902 travels. Alternatively or additionally, the second navigational information is associated with an image resolution from a camera onboard vehicle 4902 that is higher than an image resolution associated with the first navigational information. For example, the first navigational information may include one or more images captured by a camera onboard vehicle 4902 at a first resolution of 1392×1024, and the second navigational information may include one or more images captured by the camera onboard vehicle 4902 at a second resolution of 1600×1200 (or higher).

As described elsewhere in this disclosure, exemplary second navigational information may include information and/or data relating to the locations of the vehicle, speeds of the vehicle, driving directions of the vehicle, landmarks associated with one or more road segments, roads, one or more road features, one or more infrastructure objects, water features, geographic features, points of interest (e.g., office buildings, restaurants, gas stations, etc.), one or more images associated with one or more road segments, LIDAR data associated with one or more road segments, a sparse data model including polynomial representations of certain road features (e.g., lane markings), target trajectories for the host vehicle, or the like, or a combination thereof. For example, the second navigational information may include location information associated with one or more landmarks associated with the one or more road segments. Alternatively or additionally, the second navigational information may include location information associated with one or more road features associated with the one or more road segments.

In some embodiments, the first navigational information and/or second navigational information may also include video detection and ranging (VIDAR) data. For example, vehicle 4902 may include a set of cameras configured to generate a dense point cloud of the environment of the vehicle at a particular point in time using the VIDAR technology. By way of example, vehicle 4902 (and/or server 4901) may generate the cloud by learning monocular features and correlation features to infer the depth at each pixel in a subset of the cameras. In some embodiments, the first navigational information and/or second navigational information may both LIDAR and VIDAR data. In some embodiments, the LIDAR and VIDAR data may be collected around the same time.

In some embodiments, the first navigational information may include LIDAR data, and the second navigational information may include VIDAR data, which may have a greater density level than that of LIDAR data.

In some embodiments, vehicle 4902 may be configured to continue to collect the second navigational information if it is determined that vehicle 4902 is at or within the predetermined distance from the geographical region of interest (or a boundary thereof). For example, server 4901 (and/or vehicle 4902) may be configured to determine the locations of vehicle 4902 and determine whether vehicle 4902 is at or within the predetermined distance from the geographical region of interest (or a boundary thereof) as described elsewhere in this disclosure. Server 4901 may transmit an instruction to vehicle 4902 to continue to collect the second navigational information unless a subsequent instruction to switch to collecting the first navigational information is received by vehicle 4902. In some embodiments, server 4901 (and/or vehicle 4902) may be configured to determine whether vehicle 4902 travels beyond the predetermined distance from geographical region of interest 5001 after it has been determined that vehicle 4902 is at or within the predetermined distance from geographical region of interest 5001. Server 4901 (and/or vehicle 4902) may also be configured to cause vehicle 4902 to switch back to collecting the first navigational information.

At step 5105, at least one of the collected first navigational information or the collected second navigational information from the host vehicle may be uploaded. For example, server 4901 may be configured to cause vehicle 4902 to upload at least one of the collected first navigational information or the collected second navigational information (or a portion thereof) from vehicle 4902. Vehicle 4902 may upload at least one of the collected first navigational information or the collected second navigational information via a transceiver associated with vehicle 4902. In some embodiments, vehicle 4902 may transmit the first navigational information and/or second navigational information (or a portion of the first navigational information and/or a portion of the second navigational information) to server 4901 continuously. Alternatively, vehicle 4902 may transmit the first navigational information and/or second navigational information (or a portion of the first navigational information and/or a portion of the second navigational information) to server 4901 intermittently. For example, vehicle 4902 may transmit the first navigational information and/or second navigational information (or a portion of the first navigational information and/or a portion of the second navigational information) to server 4901 a number of times over a period of time. By way of example, vehicle 4902 may transmit the first navigational information and/or second navigational information (or a portion of the first navigational information and/or a portion of the second navigational information) to server 4901 once per minute. Alternatively, the vehicle may transmit the first navigational information and/or second navigational information (or a portion of the first navigational information and/or a portion of the second navigational information) when vehicle 4902 has access to a more reliable and/or faster network (e.g., having a stronger wireless signal, via a WIFI connection, etc.). In some embodiments, vehicle 4902 may the first navigational information and/or second navigational information (or a portion of the first navigational information and/or a portion of the second navigational information) upon a request received from server 4901. For example, server 4901 may transmit a request to vehicle 4902 requesting transmission of a subset of the first navigational information or the second navigational information collected by vehicle 4902. Vehicle 4902 may transmit the requested navigational information (e.g., the subset of the first navigational information or the second navigational information) based on the received request. In some embodiments, the request may include at least one time stamp.

In some embodiments, vehicle 4902 may be configured to upload both first and second navigational information to server 4901, and server 4901 may be configured to receive and store both the first navigational information and the second navigational information.

In some embodiments, server 4901 may be configured to cause vehicle 4902 to upload the collected second navigational information (or a portion thereof) based on a determination of whether vehicle 4902 traveled within the geographical region of interest. For example, as illustrated in FIG. 51C, server 4901 may be configured to determine that vehicle 4902 traveled within geographical region of interest 5001. Server 4901 may be configured to cause vehicle 4902 to upload the collected second navigational information (or a portion thereof) to server 4901. Alternatively or additionally, server 4901 may be configured to cause vehicle 4902 to upload a portion of the collected second navigational information that is associated with geographical region of interest 5001 to server 4901. In some embodiments, server 4901 may also be configured to cause vehicle 4902 to discard a portion of the collected second navigational information that is not associated with geographical region of interest 5001.

In some embodiments, server 4901 may be configured to determine that vehicle 4902 did not travel within the geographical region of interest. Server 4901 may further be configured to cause vehicle 4902 not to upload all of or at least a portion of the second navigational information if vehicle 4902 did not travel within the geographical region of interest. Alternatively or additionally, server 4901 may be configured to cause vehicle 4902 to discard (and/or overwrite) all of or at least a portion of the second navigational information (or mark all or at least a portion of the second navigational information as being eligible for being overwritten) based on the determination that vehicle 4902 did not travel within the geographical region of interest. In some embodiments, server 4901 may be configured to determine whether vehicle 4902 traveled within geographical region of interest 5001 after determining that vehicle 4902 vehicle approached to within the predetermined distance to geographical region of interest 5001 and then moved away to at least the predetermined distance away from geographical region of interest 5001.

In some embodiments, vehicle 4902 may upload the first navigational information and/or the second navigational information without input or an instruction from server 4901. For example, vehicle 4902 may transmit the first and/or second navigational information (and/or sensor data) to server 4901 when such information is collected. Alternatively, vehicle 4902 may transmit the first and/or navigational information (and/or sensor data) to server 4901 upon a trigger event. For example, vehicle 4902 may determine that a predetermined period of time or a predetermined distance has passed since the last semantic object is detected. Vehicle 4902 may also upload the first and/or second navigational information (and/or sensor data) collected to server 4901.

At step 5106, a navigational map be updated. For example, server 4901 may be configured to update the navigational map based on the uploaded at least one of the collected first navigational information or the collected second navigational information. For example, server 4901 may be configured to update the navigational map associated with the geographical region of interest (and/or other region(s) based on the received first and/or second navigational information collected by vehicle 4902. Server 4901 may also be configured to store the updated navigational map into a storage and/or a database (e.g., database 4904). In some embodiments, server 4901 may transmit the updated navigational map to one or more vehicles. For example, server 4901 may transmit the updated navigational map to vehicle 4902 via, for example, network 4905. Alternatively or additionally, server 4901 may store the updated navigational map into database 4904, and one or more vehicles (e.g., vehicle 4902) may obtain the updated navigational map from database 4904.

In some embodiments, server 4901 may update a navigational map based on (first and/or second) navigational information received from two or more vehicles. For example, server 4901 may aggregate first and/or second navigational information received from two or more vehicles that are associated with the same road segment. In some embodiments, the navigational information collected when the vehicle drives in a direction along a road segment may be different from the navigational information collected when the vehicle drives in the opposite direction even along the same road segment. Server 4901 may aggregate first and/or second navigational information received from two or more vehicles that are associated with the same road segment in the same driving direction (and/or a direction opposite to the same driving direction). For example, server 4901 may align the navigational information received from two or more vehicles that drove in the same direction along a road segment based on the feature points determined based on the navigational information. Server 4901 may also update a navigational map based on the aggregated (first and/or second) navigational information.

In some embodiments, server 4901 may be configured to determine that the update of the navigational map associated with the geographical region of interest is completed (or at least in a period of time). Server 4901 may be configured to transmit an instruction to one or more vehicles not to collect second navigational information associated with the geographical region of interest.

In some embodiments, server 4901 may use more resources to process or use the second navigational information as compared to when server 4901 processes or uses the first navigational information.

The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on other types of computer readable media, such as secondary storage devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray, or other optical drive media.

Computer programs based on the written description and disclosed methods are within the skill of an experienced developer. The various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software. For example, program sections or program modules can be designed in or by means of Net Framework, Net Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with included Java applets.

Moreover, while illustrative embodiments have been described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations as would be appreciated by those skilled in the art based on the present disclosure. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application. The examples are to be construed as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any manner, including by reordering steps and/or inserting or deleting steps. It is intended, therefore, that the specification and examples be considered as illustrative only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents. 

1. A host vehicle-based sparse map feature harvester system, comprising: at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to: receive a plurality of images captured by a camera onboard the host vehicle as the host vehicle travels along a road segment in a first direction, wherein the plurality of images are representative of an environment of the host vehicle; detect one or more semantic features represented in one or more of the plurality of images, the one or more semantic features each being associated with a predetermined object type classification; identify at least one position descriptor associated with each of the detected one or more semantic features; identify three-dimensional feature points associated with one or more detected objects represented in at least one of the plurality of images; receive position information, for each of the plurality of images, wherein the position information is indicative of a position of the camera when each of the plurality of images was captured; and cause transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the identified at least one position descriptor associated with each of the detected one or more semantic features, the identified three-dimensional feature points, and the position information.
 2. (canceled)
 3. (canceled)
 4. The system of claim 1, wherein each of the three-dimensional feature points is determined based on analysis of representations of the one or more detected objects across two or more of the plurality of images and based on an ego motion of the host vehicle, the ego motion being determined based on at least one of the plurality of images or an output of at least one ego motion sensor, the ego motion sensor including at least one of a speedometer, an accelerometer, or a GPS receiver.
 5. (canceled)
 6. The system of claim 1, wherein at least some of the one or more detected objects are associated with the one or more detected semantic features.
 7. The system of claim 1, wherein the at least one position descriptor associated with each of the detected one or more semantic features includes at least one of a two dimensional image position or a three-dimensional point location.
 8. (canceled)
 9. The system of claim 7, wherein the at least one position descriptor associated with each of the detected one or more semantic features includes the three-dimensional point location and wherein the three-dimensional point location is determined based on analysis of representations of the one or more detected objects across two or more of the plurality of images and based on an ego motion of the host vehicle, the ego motion being determined based on at least one of the plurality of images or an output of at least one ego motion sensor, the ego motion sensor including at least one of a speedometer, an accelerometer, or a GPS receiver.
 10. (canceled)
 11. The system of claim 1, wherein the remotely-located entity includes one or more processors configured to receive, in addition to the drive information transmitted by the host vehicle, drive information for the road segment from each of a plurality of other vehicles, wherein the drive information for the road segment received from each of the plurality of other vehicles includes at least one position descriptor associated with each detected semantic feature, three-dimensional feature points for detected objects, and position information associated with captured images.
 12. The system of claim 11, wherein the drive information for the road segment received from each of the plurality of other vehicles originated from drives in which the plurality of other vehicles were traveling in a same direction as the host vehicle, and wherein the memory further includes instructions that when executed by the circuitry cause the at least one processor to align one or more aspects of the drive information from the host vehicle and the drive information from the plurality of other vehicles based on the identified at least one position descriptor associated with each of the detected one or more semantic features received from the host vehicle and based on the identified at least one position descriptor associated with each of the detected one or more semantic features received as part of the drive information received from each of the plurality of other vehicles.
 13. The system of claim 11, wherein the drive information for the road segment received from each of the plurality of other vehicles originated from drives in which at least some of the plurality of other vehicles were traveling in a same direction as the host vehicle and in which at least some of the plurality of other vehicles were traveling in a direction opposite to the host vehicle, and wherein the memory further includes instructions that when executed by the circuitry cause the at least one processor to align one or more aspects of the drive information from the host vehicle and the drive information from the plurality of other vehicles, including the other vehicles that traveled the road segment in a direction opposite to the host vehicle, based on the three-dimensional feature points received from the host vehicle and based on the three-dimensional feature points received from each of the plurality of other vehicles.
 14. The system of claim 13, wherein the alignment of one or more aspects of the drive information from the host vehicle and the drive information from the plurality of other vehicles, includes correlating three-dimensional feature points acquired during drives of opposite direction.
 15. The system of claim 14, wherein the memory further includes instructions that when executed by the circuitry cause the at least one processor generate a sparse map based on the aligned drive information from the host vehicle and the drive information from the plurality of other vehicles.
 16. The system of claim 1, wherein for the road segment, a number of identified position descriptors associated with detected semantic features is greater than a number of identified three-dimensional points.
 17. The system of claim 1, wherein for the road segment, the memory further includes instructions that when executed by the circuitry cause the at least one processor to identify and transmit to the remotely-located entity no more than between 1 and 40 three-dimensional feature points per meter of the road segment.
 18. The system of claim 1, wherein the three-dimensional feature points identified by the at least one processor are associated with one or more of edges or corners of at least one surface associated the at least some of the one or more detected objects.
 19. The system of claim 1, wherein each of the three-dimensional feature points includes at least one of: an X-Y position relative to at least one of the plurality of images along with an indicator of range relative to the camera, or an X-Y-Z location relative to a real world origin.
 20. The system of claim 19, wherein the indicator of range is determined based on: tracking across two or more of the plurality of images of an image position associated with an identified object; and an ego motion signal of the host vehicle.
 21. The system of claim 1, wherein the position information includes at least one indicator of position determined based on: an output of a GPS sensor associated with the host vehicle, an ego motion of the host vehicle, or a combination of the output of the GPS sensor associated with the host vehicle and the one ego motion of the host vehicle.
 22. (canceled)
 23. (canceled)
 24. A method for harvesting sparse map features by a host vehicle, the method comprising: receiving a plurality of images captured by a camera onboard the host vehicle as the host vehicle travels along a road segment in a first direction, wherein the plurality of images are representative of an environment of the host vehicle; detecting one or more semantic features represented in one or more of the plurality of images, the one or more semantic features each being associated with a predetermined object type classification; identifying at least one position descriptor associated with each of the detected one or more semantic features; identifying three-dimensional feature points associated with a one or more detected objects represented in at least one of the plurality of images; receiving position information, for each of the plurality of images, wherein the position information is indicative of a position of the camera when each of the plurality of images was captured; and causing transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the identified at least one position descriptor associated with each of the detected one or more semantic features, the identified three-dimensional feature points, and the position information.
 25. A sparse map generator system for creating maps used in navigating autonomous or partially autonomous vehicles, comprising: at least one processor programmed to: receive first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction, wherein the first drive information includes a first plurality of three-dimensional feature points associated with objects detected by navigation systems of the first plurality of vehicles; receive second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction, wherein the second drive information includes a second plurality of three-dimensional feature points associated with objects detected by navigation systems of the second plurality of vehicles; correlate one or more of the first plurality of three-dimensional feature points with one or more of the second plurality of three-dimensional feature points; and generate a sparse map based on the correlation of the first plurality of three-dimensional feature points and the second plurality of three-dimensional feature points, the sparse map including at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction.
 26. The system of claim 25, wherein each of the first drive information and the second drive information includes no more than between 1 and 40 three-dimensional feature points received from any one of the first plurality of vehicles or the second plurality of vehicles per meter of the road segment.
 27. The system of claim 25, wherein generation of the sparse map is based on a correlation of at least 100 three-dimensional feature points per meter of the road segment.
 28. (canceled)
 29. The system of claim 25, wherein the first drive information includes a first plurality of identified position descriptors associated with semantic features detected by navigation systems of the first plurality of vehicles, the second drive information includes a second plurality of identified position descriptors associated with semantic features detected by navigation systems of the second plurality of vehicles, and wherein the generation of the sparse map is further based on a correlation of the first plurality of identified position descriptors associated with semantic features and the second plurality of identified position descriptors associated with semantic features determined by the at least one processor.
 30. (canceled)
 31. The system of claim 25, wherein the three-dimensional feature points of the first drive information and the second drive information are associated with one or more of edges or corners of at least one surface associated with the objects detected by navigation systems of the first and second plurality of vehicles.
 32. The system of claim 25, wherein the first plurality and the second plurality of three-dimensional feature points include at least one of: an X-Y position relative to at least one image along with an indicator of range relative to a camera or an X-Y-Z position relative to a predetermined origin.
 33. The system of claim 32, wherein the indicator of range is determined based on: tracking across two or more plurality of images of an image position associated with an identified object; and an ego motion signal of a host vehicle.
 34. (canceled)
 35. The system of claim 32, wherein the X-Y-Z position is determined based on: tracking across two or more plurality of images of an image position associated with an identified object; and an ego motion signal of a host vehicle.
 36. The system of claim 25, wherein the first drive information includes first camera position information associated with a first plurality of captured images, the second drive information includes second camera position information associated with a second plurality of captured images, and wherein the generation of the sparse map is further based on the first camera position information and the second camera position information.
 37. The system of claim 36, wherein the first camera position information and the second camera position information include at least one indicator of position determined based on: an output of a GPS sensor, an ego motion of the first plurality of vehicles and the second plurality of vehicles, or a combination of the output of the GPS sensor and the ego motion of the first plurality of vehicles and the second plurality of vehicles.
 38. (canceled)
 39. (canceled)
 40. A method for creating maps used in navigating autonomous or partially autonomous vehicles, the method comprising: receiving first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction, wherein the first drive information includes a first plurality of three-dimensional feature points associated with objects detected by navigation systems of the first plurality of vehicles; receiving second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction, wherein the second drive information includes a second plurality of three-dimensional feature points associated with objects detected by navigation systems of the second plurality of vehicles; correlating one or more of the first plurality of three-dimensional feature points with one or more of the second plurality of three-dimensional feature points; and generating a sparse map based on the correlation of the first plurality of three-dimensional feature points and the second plurality of three-dimensional feature points, the sparse map including at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction. 41-134. (canceled)
 135. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, are configured to cause at least one processor to perform a method for harvesting sparse map features by a host vehicle, the method comprising: receiving a plurality of images captured by a camera onboard the host vehicle as the host vehicle travels along a road segment in a first direction, wherein the plurality of images are representative of an environment of the host vehicle; detecting one or more semantic features represented in one or more of the plurality of images, the one or more semantic features each being associated with a predetermined object type classification; identifying at least one position descriptor associated with each of the detected one or more semantic features; identifying three-dimensional feature points associated with a one or more detected objects represented in at least one of the plurality of images; receiving position information, for each of the plurality of images, wherein the position information is indicative of a position of the camera when each of the plurality of images was captured; and causing transmission of drive information for the road segment to an entity remotely-located relative to the host vehicle, wherein the drive information includes the identified at least one position descriptor associated with each of the detected one or more semantic features, the identified three-dimensional feature points, and the position information.
 136. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, are configured to cause at least one processor to perform a method for creating maps used in navigating autonomous or partially autonomous vehicles, the method comprising: receiving first drive information for a road segment transmitted by a first plurality of vehicles that traveled the road segment in a first direction, wherein the first drive information includes a first plurality of three-dimensional feature points associated with objects detected by navigation systems of the first plurality of vehicles; receiving second drive information for the road segment transmitted by a second plurality of vehicles that traveled the road segment in a second direction opposite to the first direction, wherein the second drive information includes a second plurality of three-dimensional feature points associated with objects detected by navigation systems of the second plurality of vehicles; correlating one or more of the first plurality of three-dimensional feature points with one or more of the second plurality of three-dimensional feature points; and generating a sparse map based on the correlation of the first plurality of three-dimensional feature points and the second plurality of three-dimensional feature points, the sparse map including at least a first target trajectory for a lane of travel along the road segment in the first direction and at least a second target trajectory for a lane of travel along the road segment in the second direction. 